WO2010058584A1 - System, method and program for making composition plan and allocation of ships - Google Patents

System, method and program for making composition plan and allocation of ships Download PDF

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Publication number
WO2010058584A1
WO2010058584A1 PCT/JP2009/006236 JP2009006236W WO2010058584A1 WO 2010058584 A1 WO2010058584 A1 WO 2010058584A1 JP 2009006236 W JP2009006236 W JP 2009006236W WO 2010058584 A1 WO2010058584 A1 WO 2010058584A1
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Prior art keywords
ship
plan
blending
raw material
amount
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PCT/JP2009/006236
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French (fr)
Japanese (ja)
Inventor
小林敬和
屋地靖人
岡本哲也
山田祈一
佐野拓也
潮田泰宏
渡辺裕司
金澤典一
佐藤智宏
斎藤元治
鈴木豊
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新日本製鐵株式会社
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First worldwide family litigation filed litigation Critical https://patents.darts-ip.com/?family=42198027&utm_source=***_patent&utm_medium=platform_link&utm_campaign=public_patent_search&patent=WO2010058584(A1) "Global patent litigation dataset” by Darts-ip is licensed under a Creative Commons Attribution 4.0 International License.
Application filed by 新日本製鐵株式会社 filed Critical 新日本製鐵株式会社
Priority to CN2009801337236A priority Critical patent/CN102137803B/en
Priority to BRPI0920989A priority patent/BRPI0920989A2/en
Priority to KR1020117004465A priority patent/KR101296933B1/en
Priority to JP2010510606A priority patent/JP4669582B2/en
Publication of WO2010058584A1 publication Critical patent/WO2010058584A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G61/00—Use of pick-up or transfer devices or of manipulators for stacking or de-stacking articles not otherwise provided for

Definitions

  • the simulation unit simulates the operation of each means of transportation based on the constraint condition, and the raw material inventory amount transition calculation unit calculates each material brand based on the simulation result. The change of the stock quantity of The result of this calculation is evaluated by the evaluation value calculation unit.
  • the first blending plan creation unit further calculates a planned use amount of the blended raw materials according to the created blending plan
  • the ship allocation plan creation unit is: a group in which the combination raw materials of the plurality of brands included in the range defined by the planned usage amount of the mixed raw materials, the take-up target amount of the mixed raw materials, and properties are handled as a group Stock status of chemical compound raw materials, purchase cost of the compound raw materials, ship list in which a plurality of ships operated based on a plurality of types of chartering contracts are listed, operation status of each of the ships, and transportation costs,
  • a data capturing unit that captures data including a ship financial resource list that selects a ship that is a candidate for dispatch from the ship list based on the operational status and creates a ship financial resource list
  • a creation unit may be further included, and the database unit may further store the second formulation plan.
  • a method according to another aspect of the present invention for achieving the above object includes a ship allocation plan for transporting a plurality of branded raw materials from a plurality of loading sites to a plurality of landing sites; A blending plan for mixing the blended raw materials at each of the landings; and a blending / shipping plan creation method for creating: a collection target of the blended raw materials set in advance for each loading place and each brand A first blending plan creation step for creating a first blending plan based on the quantity; a shipping plan creation step for creating the ship assignment plan based on the created first blending plan; A database storage step of storing the ship allocation plan in a database.
  • a program according to another aspect of the present invention for achieving the above object is a program for causing a computer to function as each part of the blending and ship
  • a blending plan for receiving and mixing a plurality of branded raw materials and a ship allocation plan for transporting a plurality of branded raw materials from a plurality of loading sites to a plurality of landing sites are linked to each other.
  • transportation including the type of chartering contract of the ship continuous voyage ship, irregular ship, spot ship), whether or not to hire a ship that determines the composition of the fleet (preparation of ship funding list), etc. It is possible to plan for minimizing costs.
  • FIG. 1 is a diagram showing a schematic configuration of a composition and ship allocation plan creation system according to Embodiments 1 to 4 of the present invention.
  • a second blending plan creation apparatus 300 is shown, but in the first embodiment, this is not used.
  • Reference numeral 100 denotes a first blending plan creation device, which creates a blending plan for receiving and mixing a plurality of brand raw materials based on a raw material take-up target amount.
  • a blending plan for blending raw materials for each steelworks is created.
  • a planned usage amount is planned which indicates how much raw materials are used every day at each steelworks.
  • blending plan preparation apparatus 100 functions as a 1st mixing
  • Reference numeral 200 denotes a ship allocation plan creation device. Based on the formulation plan created by the first formulation plan creation device 100, a plurality of brands of raw materials (ores, coals, etc.) are scattered in a plurality of places (in the world). Create a ship allocation plan for transportation from Yamamoto) to multiple landing sites (steel works). The purpose of this embodiment is to create a ship allocation plan that minimizes the transportation cost of all steelworks, not the leveling of transportation costs for each steelworks. Furthermore, the purpose is to create a ship allocation plan that minimizes the cost including the purchase cost of raw materials in addition to the transportation cost.
  • This ship allocation plan creation apparatus 200 functions as a ship allocation plan creation part as referred to in the present invention.
  • the first blending plan creation device 100 is constructed according to mathematical programming methods such as LP (linear programming), MIP (mixed integer programming), QP (quadratic programming), Optimize the formulation plan by setting a mathematical model that represents the supply-demand balance constraint for raw materials (also referred to as “supply-demand balance model”) and a mathematical model that represents the property constraint after mixing (also referred to as “property model”) .
  • the setting of the mathematical model is a series of steps also called development of the mathematical model, and includes the steps described below, and is performed by each unit or method of the apparatus according to the present embodiment.
  • the usage amount (mixing ratio) of each brand, the amount received, the inventory transition graph, and various forms obtained by the first blending plan creation device 100 are displayed.
  • the operator evaluation unit 304 the operator evaluates the obtained blending plan from various viewpoints (for example, inventory transition, properties, etc.), and corrects the blending ratio and the like as necessary if the result is not satisfactory. . At that time, the weight of the objective function and the evaluation index are changed as necessary, and the target period / plan decision period for setting the mathematical model is changed. In addition, it is possible to set a mathematical model reflecting the operator's will, such as fixing the amount of use only for all or specified processes. Then, the blending plan is created again by the first blending plan creation device 100.
  • viewpoints for example, inventory transition, properties, etc.
  • FIG. 3 is a block diagram showing the basic configuration of the first formulation planning device.
  • the first blend plan creation device 100 includes a simulator (including an inventory transition simulator 311 and a property simulator 312), a model setting unit (demand / supply balance model setting) that functions as a mathematical model setting unit in the present invention. 313, a property model setting unit 314), and a planning unit 315 functioning as an optimization calculation unit in the present invention, and further includes an input / output unit.
  • the mathematical model setting process is performed based on the input data 316 including the following information: a plan creation period, a take-up target amount, a raw material inventory status, which are necessary for formulating a formulation plan, Properties of raw materials, purchase cost information indicating the unit price of raw materials, and transportation cost information when using a ship.
  • LP Linear Programming
  • MIP Mated Integer Programming
  • QP Secondary Programming
  • a mathematical model representing supply / demand balance constraints is set by the supply / demand balance model setting unit 313, and a mathematical model representing property constraints is set by the property model setting unit 314.
  • calculation is not performed based on a predetermined rule as in the prior art, but calculation based on the result of optimization calculation performed by the planning unit 315.
  • the instruction is output to the inventory transition simulator 311 and the property simulator 312. For this reason, it is possible to reliably perform an optimal calculation instruction according to the event at that time.
  • each simulator the inventory transition simulator 311 and the property simulator 312
  • each model setting unit the supply and demand balance model setting unit 313, the property model setting unit 314.
  • the planning unit 315 By executing this, it is possible to create an optimal blending plan. That is, the simulation performed in the present embodiment is performed based on the result of the optimization calculation, not the simulation based on the predetermined rule as in the prior art. For this reason, it is possible to surely obtain a theoretically optimal solution by executing only one simulation. With this configuration, it is not necessary to evaluate the simulation result and repeat the simulation many times as in the prior art, and the simulation result 317 can be created quickly and with high accuracy. Therefore, even if the target for creating the formulation plan is large, it can be sufficiently created within the practical time.
  • the problem scale can be reduced by dividing the entire plan creation period into shorter optimization periods. For this reason, even if the plan preparation period as a whole becomes long, it becomes possible to solve a problem.
  • the simulation result 317 obtained as described above is output as a blending plan.
  • the stock status of raw materials is information indicating the stock quantity (ton tonnage) by steelworks and brand on the first day of the planning period.
  • the property of the raw material is information indicating the property of the component for each raw material.
  • the property of iron ore that is a raw material includes property information on raw materials such as Fe 2 O 3 , Fe 3 O 4 , SiO 2 , and Al 2 O 3 .
  • the raw material purchase cost information is information indicating the unit price ($ / ton (ton, t)) of the raw material by Yamamoto (loading place) and by brand.
  • the transportation cost information when using a ship uses a freight when using a ship listed in the ship list and a ship listed in the ship list. This is information indicating the berthing fee for each port of loading.
  • the transportation cost information when using a ship includes information indicating the freight rate by brand and landing port. The transportation cost is uniquely determined by the freight rate by ship, by port, and by port. However, in the operation phase of the first blending plan creation device 100, the ship on which the raw material is loaded is not determined for the arrival. In order to estimate the transportation cost of raw materials for those raw materials that have not yet been decided by the ship to be loaded, it is necessary to have a freight by brand / shipping port.
  • Equation 7 the constraint equation representing the relationship between the usage amount, the usage target amount, the overflow amount, and the shortage amount of each brand is expressed as the following (Equation 7). That is, if overflow occurs, the overflow amount is subtracted from the usage amount, and if there is a shortage, the shortage amount is added to match the target usage amount.
  • a weighted average that is a linear mathematical expression f ′ (90, 0, 10,..., 0) is represented by the following expression.
  • f ′ (90, 0, 10,..., 0) 0.9 x f (100, 0, ..., 0) + 0.1 x f (0, 0, 100, ... 0)
  • this weighted average satisfies (Equation 16)
  • this weighted average is used as a linear equation f ′ (x A , x B , x C ,..., X N ). can do. That is, if weighted average ⁇ S is a constraint, it is possible to formulate by assuming that (Equation 14) holds.
  • the supply / demand balance model setting unit 356 (supply / demand balance model setting unit 313 in FIG. 3) and step S306, the property model setting unit 357 (properties model setting unit 314 in FIG. 3), and steps S307 and S307a described above are included in the present invention. It is the numerical formula model setting part of the 1st mixing
  • Immobilization extraction processing (immobilization extraction processing unit 358 in FIG. 4, step S308 in FIG. 5)
  • items that are fixed that is, those that cannot be changed, are extracted from among the shipping port items, loading brands, loadings, landing ports, lifting brands, and lifting amounts, which are items of the ship allocation plan.
  • the “loading port” and “loading brand” are fixed for each dredger according to the conditions given in advance, the freight rate for each ship, each loading port, and each lifting port (see FIG. 15) is used.
  • these dredgers have been determined to be loaded with raw materials, by using the freight by ship, by port, by port, and when the steelworks that will receive the raw materials is determined by optimization, Accurate transportation cost calculation is possible.
  • This immobilization extraction process does not need to be at the timing shown in FIG. 5, and may be performed, for example, when the formulation plan creation is started.
  • the collection target amount of each brand is tabulated in seasonal units (or monthly units), and the accumulation up to that time is set as the target value (collection target amount accumulation). Then, an objective function is constructed so as to minimize the difference between the arrival amount accumulation and the pickup target amount accumulation. In other words, taking into account take-up target amount accumulation and take-up amount accumulation, the object is also to reduce the difference between the take-up target amount accumulation and the take-up amount accumulation. In this embodiment, considering the accumulation, an item for minimizing the sum of the overflow amount and the deficiency amount from the seasonal collection target accumulation amount is added to the objective function.
  • step S401 the supply-demand balance model, texture model (nonlinear equation f (x A, x B, x C, ⁇ , instead of x N) linear equations f'(x A, x B, x C, .., X N ) are introduced), and the optimization calculation is executed based on the objective function J.
  • step S402 the solution obtained by the optimization calculation using the mathematical model f ′ (x A , x B , x C ,..., X N ) ⁇ S ′ including the linear mathematical formula is a nonlinear mathematical formula. It is determined whether or not the included mathematical model f (x A , x B , x C ,..., X N ) ⁇ S is satisfied. That is, the result of the optimization calculation in step S401 (the usage amount (mixing ratio) of each brand A to N) is substituted into (Expression 14), and it is determined whether (Expression 14) is satisfied.
  • the linear mathematical formula f ′ (x A , x B , x C ,..., X N ) is the upper limit of the nonlinear mathematical formula f (x A , x B , x C ,..., X N ).
  • the nonlinear mathematical formula f (x A , x B , x C ,. , X N ) is set to a temporary upper limit value that is larger than the upper limit value.
  • a long-term plan such as an annual plan, a term plan, or a monthly plan is formulated as a blending plan (for example, usage (mixing ratio)).
  • a long-term blending plan is created in advance, the blending plan is used as a reference blending plan, and the shorter-term blending plan created by the first blending plan creation device 100 is more than a certain distance away from the reference blending plan. It is also important to avoid it.
  • the ship allocation plan creation device 200 fetches, for example, the following data from the database 400: scheduled use amount of raw materials (amount used by the blending plan created by the first blending plan creation device 100), take-up target amount, chartering contract Ship list with different types of ships listed, operation status of ships listed in ship list, stock status of raw materials, purchase cost information indicating unit price of raw materials, ship listed in ship list Transportation cost information when used.
  • the ship allocation plan creating apparatus 200 creates a ship allocation plan for, for example, three months (September) based on the acquired data.
  • seasonal refers to the unit of period divided into three months.
  • 201 is a simulator that simulates ship operation, loading and unloading facilities, yards, and the like.
  • the simulator 201 receives input information determined by a macro optimization unit 202 and a micro optimization unit 203, which will be described later, and executes a detailed simulation based on the input information.
  • This input information includes the voyage of vessels, non-regular ships, and spot ships (shipping port), loading brands, amount of loading, arrival order, berthing berth, entry / exit timing, and spot ship to be hired. Contains information on the number and type of ship (the size of the ship defined based on the maximum load capacity of the ship).
  • This simulator is composed of an inventory transition simulator and a ship operation status transition simulator.
  • FIG. 12 is a flowchart for explaining the steps of each process in the ship assignment plan creation method using the ship assignment plan creation apparatus 200.
  • a ship allocation plan is created with a plan creation period of 3 months (9 seasons) from the planning start date set by the user.
  • the data acquisition unit 204 of the ship allocation plan creation device 200 includes a ship list in which ships with different raw material usage schedules, take-up target quantities, and contract types are listed from the database 400, and ships listed in the ship list. Operational status, raw material inventory status, raw material purchase cost, transportation cost when using a ship listed in the ship list, ship berthing status at loading site, loading capacity status, facility repair / suspension schedule, lifting Capture data such as ship berthing status, unloading capacity status, facility repair / outage schedule, etc.
  • the hull form represents the size of a ship defined based on the maximum load capacity of the ship.
  • Pmax which represents the ship type of a spot ship, is a ship that can pass through the Panama Canal (generally this ship type is called Panamax)
  • Cape is a ship that can pass Cape Cape (generally this ship type is called Cape size)
  • VL Very Large
  • the normal Panamax is a ship that is 900 feet long and 106 feet wide and has a maximum load capacity of 60,000 to 80,000 tons.
  • the normal cape size refers to ships with a maximum capacity or capacity of 150,000 to 170,000 tons.
  • Fig. 16 shows an example of a list of berthing charges.
  • a dredger code for each ship listed in the ship list, a lift run (t / Day), and a desdemarate ($ / day) are described.
  • Lifting rate is the standard capacity for unloading and represents the amount that can be handled in one day. Compared to the case where it is assumed that the cargo can be lifted with the lifting capacity, when the actual lifting time is shortened, the amount set in the desdemaration rate can be received from the shipping company. On the contrary, if it becomes late, the amount set for the death demarcation will be paid to the shipping company.
  • Death Demarcation is a term that also refers to the contracted fee rate for charges or refunds that are made in the event of an early departure (Despatch) or a berthing (Demage) at a loading / unloading port. is there.
  • Despatch early departure
  • Demage berthing
  • the description of demarcation rate is also used. For example, let us consider a case where ETD 11 hours after ETA when a continuous cruise ship A unloads 10,000 tons. Since the lifting run is 20000 (t / Day), the unloading is expected to take 12 hours.
  • the ship financial resource list creation unit 202a of the macro optimization unit 202 is a ship that is or may be a target of subsequent processing of the ship allocation plan from the ship list (see FIG. 13) captured in step S101. To create a ship funding list.
  • FIG. 17 is a flowchart for explaining a ship selection process.
  • the ship funding list creation unit 202a first extracts a continuous sailing ship having an undetermined portion scheduled for operation in the plan creation period based on the ship list (see FIG. 13) and the ship operation status (see FIG. 14) (Ste S201). For example, assuming that the planning start date is March 1, 2008 and a ship allocation plan for three months is to be created, as shown in FIG. 14, the continuous cruise ship A has not been determined since April 18, 2008. Therefore, the continuous cruise ship A is extracted.
  • FIG. 18 shows the voyage No. already confirmed. 3 (“A-3” in the figure), the pattern of loading port X2 to unloading port A (voyage No. 4) and loading port X1 to unloading port B (voyage No. 5) is shown.
  • each time is obtained using a standard voyage time (distance between ports and a standard knot of the ship A) and a standard loading time.
  • voyage no. The time of landing at Yacht A in No. 4 is [voyage no. 4 at the time of landing at loading port X2] + [standard loading time] + [distance between port X2 and port A] / [standard knot of vessel A].
  • all of these patterns are created. The same operation will be performed for the other continuous cruise ships.
  • an irregular ship that can be used in the plan creation period and has an undetermined portion is extracted (step S203). For example, as shown in FIG. 13, since the scheduled allocation date of the irregular ship 5 is out of the plan creation period, the irregular ship 5 is not extracted. Then, as in the case of a continuous voyage ship, all patterns of combination of loading and unloading sites (or all patterns that meet specific conditions) are created for each extracted irregular ship in the plan creation period (step S204). .
  • spot ship candidates are extracted based on the ship list (see FIG. 13) (step S205). Specifically, first, the total take-off target amount in the plan creation period is calculated. Further, the sum of the maximum loading capacity of the continuous voyage ship and the irregular ship extracted in steps S201 and S202 is calculated. As a result, the transport volume to be supplemented by the spot ship can be calculated by subtracting the total of the maximum loading capacity of the continuous voyage ship and the irregular ship in the planning period from the total take-up target quantity (Fig. 19). Based on the transport amount to be supplemented by this spot ship, the maximum load capacity of each spot ship is referred to calculate how many spot ships are required, and the minimum number of each spot ship is obtained.
  • the transport amount to be supplemented by the spot ship is 250,000 tons
  • four Australia-PmaxSpots are candidates for a spot ship to be contracted.
  • the minimum number of spot ships is obtained as in CapSpot.
  • the minimum number of spot ships obtained here is the minimum number of spot ships required when the take-up is supplemented only with the spot ship of the dredger code. As will be described later, more spot ships may be required than the minimum number of ships.
  • spot ship candidates are extracted based on the ship list (see FIG. 13) and the ship operation status (see FIG. 14).
  • the ship is extracted as a candidate for a spot ship, and further, the date set in advance for each charter code for which the contract classification of the ship list is not contracted.
  • FIG. 20 shows an example of a route list of spot ships with an interval for creating spot ship candidates for each charter code as 10 days.
  • the spot ship candidates are created by narrowing the interval for creating spot ship candidates so that the number of ships is larger than the calculated minimum number of ships. Then, as in the case of a continuous voyage ship, create all patterns of combination of loading and unloading sites (or all patterns that match specific conditions) for each candidate spot ship created during the planning period. (Step S206).
  • the spot ship candidate it is determined whether to hire or not hire each spot ship candidate, and the required ship type (the size of the ship defined based on the maximum load capacity of the ship), the number of ships
  • the spot ship is determined. For example, Australia-PmaxSpot-voyage No. After 3 is created as a candidate, it may be planned not to hire in macro optimization.
  • the mathematical model setting unit 202b of the macro optimization unit 202 sets a mathematical model constructed so as to represent the ship operation restriction, the supply and demand balance restriction of raw materials at the landing, and the take-up target quantity restriction created in step S102.
  • the mathematical model to be set is constructed (formulated) as a model according to mathematical programming such as LP (linear programming), MIP (mixed integer programming), QP (quadratic programming), and the like.
  • LP linear programming
  • MIP mixed integer programming
  • QP quadrattic programming
  • the setting of the mathematical model refers to the maximum number of subscripts in each array (for example, the number of ships) for the basic mathematical model that is constructed in an abstract format so that it can cope with changes in the number of ships, the number of ports, etc. ) And the coefficient values in the formula are specifically determined according to the actual plan.
  • the continuous cruise ship A shown in FIG. 4 (“A-4” in the figure), if there are two ports X1 and X2 where the ship can call, define the following two integer variables to correspond to each port To do.
  • ETA which is the third subscript of these integer variables, is the offshore time calculated in step S102.
  • variable indicating the amount that the ship unloads the brand in the loading port, the landing port, and the calling order is defined.
  • Constraint conditions indicating conditions such as “the load capacity of each ship does not exceed the maximum load capacity” and “the load capacity must be completely unloaded” are constructed in advance as a basic mathematical model.
  • a series of steps including optimization (steps S103 to S106) and simulation (S107) can be executed by repeating a plurality of loops.
  • the ship operation restrictions are set in the mathematical model based on the data captured in step S101.
  • the simulator 201 sets a mathematical model reflecting the simulation result performed in the previous loop. In this case, the mathematical model is set such that the load capacity of each ship does not exceed the maximum load capacity, the entire load capacity is unloaded, and the like.
  • a constraint condition that “the stock amount of each brand is always secured more than the safety stock amount” as shown in FIG. 21 is constructed as a mathematical model. .
  • this mathematical model is set based on the data fetched in step S101 and reflecting the simulation result in the simulator 201 in the subsequent loop (steps S103 to S107) and thereafter.
  • the take-up target amount restriction is set in the initial loop by reflecting the simulation result in the simulator 201 after the next loop (steps S103 to S107) after the data fetched in step S101.
  • the takeover amount (loading amount) to be optimized should not deviate from the takeover target amount by more than a certain width, whether or not it can be picked up (as mentioned above, for a given brand, it will not be taken out for a given period Etc.) are built into the mathematical model.
  • the restriction that the pick-up amount does not deviate from the pick-up target amount by a certain width or more for example, as shown in FIG.
  • upper and lower limit values are simply set for the pick-up target amount every season (or every month)
  • the loading amount does not exceed the upper and lower limit values.
  • the take-up crack may occur if accumulated for the year. Therefore, as shown in FIG. 22B, taking into account the collection target amount accumulation and the collection amount accumulation every season (or every month), the difference between the collection target amount accumulation and the collection amount accumulation is reduced (minimized, up)
  • a constraint such that the lower limit value is not exceeded.
  • a variable indicating an overflow amount and a deficiency amount from the seasonal collection target cumulative amount is defined.
  • the constraint equation representing the accumulated amount of each brand is expressed as (Equation 26) below. That is, the collected amount is the sum of the unloading amount of the ship (voyage) in which the ETA is entered during the period from the planning start date to the season.
  • Equation 27 The constraint equation that expresses the relationship between the accumulation of the collection target amount of each brand, the overflow amount, and the shortage amount is expressed as (Equation 27) below. That is, if the overflow amount is subtracted from the take-up accumulated amount, and if the deficiency is added, the deficit amount is added.
  • the overflow amount and the shortage amount are added as items of the objective function, and are minimized by optimization.
  • an integer variable indicating the order of port calls is introduced.
  • This port-calling variable is 1, which indicates a case where a specific ship selects a combination of a specific loading port as a loading port, a corresponding unloading port 1 as a first unloading port, and a specific unloading port 2 as a second unloading port. It takes one of the values 0, which indicates the case where the combination port order is not selected.
  • the optimization calculation unit 202c of the macro optimization unit 202 performs optimization calculation based on the objective function (evaluation function) constructed with respect to the transportation cost, using the mathematical model set in step S103.
  • the problem is solved as an optimization problem by mathematical programming such as LP (Linear Programming), MIP (Mixed Integer Programming), and QP (Secondary Programming).
  • variable values are determined using an objective function for the purpose of minimizing the total amount of freight in the transportation cost.
  • the hull type (the size of the ship defined based on the maximum loading capacity of the ship), the number of ships, the landing site (shipping port), the loading brand, the loading quantity that makes the total amount of freight the cheapest Is selected.
  • the freight applied to the ship is the sum of the standard freight from the loading port to the landing port calling at the first port and the multi-port additional freight applied when calling to another port as described above. Then, it is the product of the total of the multi-port lift additional freight that occurs when an extra call is made from the first port to the second port, and the amount loaded.
  • the macro optimization is intended to reduce the difference between the take-up target amount accumulation and the take-up amount accumulation in consideration of the take-up target amount accumulation and the take-up amount accumulation. For this reason, an item for minimizing the total amount of overflow and deficiency from the seasonal collection target cumulative amount is added to the objective function. Therefore, (Equation 31) representing the objective function is changed to the following equation (Equation 32). Macro optimization optimizes problems related to dredgers as a whole.
  • the formula to be minimized is formulated as an objective function, and each formula to be satisfied is formulated as a constraint formula.
  • the constraint equation is expressed by a linear equation or an inequality
  • the objective function is expressed by a linear equation.
  • a mathematical model and an objective function are constructed as models in which there are variables that should be integers. The problem formulated in this way is generally well known as a mixed integer programming problem, and this problem can be (analytical) optimized.
  • the mathematical model setting unit 203a of the micro-optimization unit 203 is a ship operation constraint in the ship allocation plan in the plan determination period obtained by the macro optimization unit 202, and a balance between supply and demand of raw materials at the landing site.
  • the mathematical model used here is constructed in accordance with a mathematical programming method such as LP (linear programming), MIP (mixed integer programming), QP (quadratic programming), or the like.
  • LP linear programming
  • MIP mixed integer programming
  • QP quadrattic programming
  • the port of call is determined by macro optimization.
  • a variable for selecting which berth of the port will be defined. This variable is an integer variable that takes one value of 1 indicating that the berth is selected and 0 indicating that the berth is not selected.
  • a variable that indicates the time (ETA) at which the ship will start offshore to arrive at the berth Since the time cannot be directly defined as a variable for formulation by MIP, it is defined as the elapsed time from the planning start date. That is, when the planning start date is 0:00 on January 1, and the ETA is 1:10 on January 1, it is defined as taking 70. Also, this variable is not an integer variable, but defined as a variable that takes a continuous value.
  • variable that indicates the stock quantity of the brand at the port of discharge is defined.
  • an objective function for minimizing the total amount of berthing charges is used, ⁇ (ship, berth) indicating whether or not the ship shores, ETA (ship, berth) indicating ETA time. ), ETB representing the ETB time (ship, berth), and ETD representing the ETD time (ship, berth).
  • the berthing charge on the ship is compared with the ETD-ETA and the contracted standard berth time, and if the berth is longer than the standard berth time, that is, if ETD-ETA> the standard berth time, the desdemaration rate In the opposite case, the contracted cost will be received as a de-demare rate.
  • the optimization period is 10 days (1st) and the time accuracy is calculated as minute accuracy.
  • Simulation (step S107) The simulator 201 executes a simulation based on the solution to the mathematical model obtained by the micro optimization unit 203, and determines the ship allocation plan for the plan determination period (in the first season).
  • the time accuracy of the simulation is minute accuracy. In this simulation, it is possible to create a ship allocation plan that takes into account even the fine constraints actually required by incorporating constraints that could not be incorporated into the macro mathematical model and the micro mathematical model.
  • one example of constraints that are difficult to handle with macro / micro optimization is the number of unloaders used to unload a single ship. This radix varies depending on the position of the hatch where the brand to be unloaded is loaded, whether or not there is a ship handling at another berth at the same port.
  • the unloading capacity varies depending on the number of unloaders used for unloading. For example, “When unloading with one unit, it is fried at 100% capacity at 1500 t / h”, or “When it is two units, it is fried at 1500 t / h ⁇ 2 units with 70% capacity” It can be illustrated.
  • the micro optimization unit 203 adjusts the time by changing the timing of entering / leaving the ship, the time is corrected by reflecting it in a spillover manner.
  • time adjustment is made at a certain port, it will affect the subsequent voyage, so the time will be corrected by the simulator 201 and reflected in the subsequent processing by the macro optimization unit 202. ing.
  • step S108 it is determined whether or not plans for the plan creation period (3 months (9 months)) have been finalized. If it has not been confirmed yet, the first day of the next season in which the plan is confirmed, for example, if the plan for N season is confirmed, the first day of N + 1 season is updated as the plan update date (step S109), and the process returns to step S103.
  • the inventory transition in the season (N season) when the plan is finalized and the operational status of the ship are updated to finalize the plan for the next season (N + 1 season). By repeating this, the plan for the plan creation period (3 months) is confirmed (see FIG. 23).
  • Shipment plan output (step S110) The ship allocation plan created as described above is displayed on a screen (not shown) by the output unit 205 or transmitted to an external device including the database 400.
  • the macro optimization unit 202 and the micro optimization unit 203 first set a mathematical model based on the initial value (initial condition) obtained from the input data or the previous loop, perform optimization calculation, An instruction for the simulator 201 is calculated.
  • the macro optimization unit 202 and the micro optimization unit 203 provide information on the transition of raw material inventory and the operational status of the ship in the final state of the plan finalization period.
  • the macro optimization unit 202 and the micro optimization unit 203 set a mathematical model based on the given information, perform optimization calculation, and calculate an instruction for the simulator. In this way, by linking the simulator 201 and the optimization units 202 and 203, it is possible to create a ship allocation plan for the plan creation period (3 months (9th September)).
  • the macro optimization unit 202 sets the ship type (the size of the ship defined based on the maximum load capacity of the ship), the number of ships, the landing site (unloading port), the unloading brand, and unloading.
  • the user can specify the ship type (the size of the ship defined based on the maximum load capacity of the ship), the number of ships, the landing site (shipping port), the loading brand, and the loading volume. May be fixed individually. This is because, for example, there are circumstances in which a predetermined ship is used or a predetermined loading port is used.
  • the ship type the size of the ship defined based on the maximum load capacity of the ship
  • the number of ships the number of ships
  • the landing site shipment port
  • the loading brand the loading volume.
  • the second blending plan creation apparatus 300 creates a blending plan for receiving and mixing a plurality of branded blending raw materials based on the dispatching plan created by the dispatching plan creation apparatus 200.
  • the configuration and basic processing operations of the second blending plan creation apparatus 300 are the same as those of the first blending plan creation apparatus 100, and will be described here with reference to FIGS.
  • FIG. 2 is a diagram showing a system configuration example including the second blending plan creation device 300.
  • the second blending plan creation device 300 sets the following constraint and precondition data necessary for formulating the blending plan by the operator.
  • Represents the plan creation period, the arrival schedule of raw materials (the amount of arrival by the ship allocation plan created by the ship allocation plan creation device 200), the stock status of the raw materials, the properties of the raw materials, and the unit price of the raw materials Includes purchase cost information and transportation cost information when using a ship.
  • the second blending plan creation device 300 creates a mixing plan for receiving and mixing various types (multiple brands) of raw materials by executing a simulation.
  • This mixing plan includes the usage amount (mixing ratio) of each brand so as to satisfy the supply-demand balance constraint of raw materials and the property constraint after mixing.
  • raw materials constructed in accordance with mathematical programming methods such as LP (Linear Programming), MIP (Mixed Integer Programming), QP (Secondary Programming), etc.
  • the blending plan was created so that the accumulated amount of the received amount did not significantly deviate from the collection target accumulated amount.
  • the arrival amount is a known amount because it is determined in advance by the ship allocation plan creation device 200 as an unloading amount for each ship. For this reason, in the 2nd mixing
  • the shipping plan is created using the use plan created by the first blending plan creating device 100 as input data of the shipping plan creating device 200.
  • the first formulation plan creation device 100 creates a plan based on the take-up target cumulative amount.
  • the take-up target cumulative amount is set to 50,000 tons with respect to the raw material X
  • the use plan and the arrival plan are planned as a cumulative amount of 50,000 tons that is the same as the take-up target cumulative amount.
  • the arrival quantity is 4.5
  • the first blending plan creation device 100 creates a blending plan based on the take-up target amount, and creates a ship allocation plan based on the created usage plan.
  • the take-up target amount and the maximum load capacity of the ship do not match, the phenomenon as in the above example can occur.
  • the blending plan is corrected by the second blending plan creation device 300 using, as input data, the dispatching plan determined by the dispatching plan creation device 100, not the take-up target amount. To do. As a result, it is possible to prevent out of stock that may occur because the delicate take-up target amount and the maximum load capacity of the ship do not match.
  • the fare of the ship is taken into account, but the usage amount planned by the first combination plan creation device based on the take-off target amount without taking into account the detailed operation status such as the berthing time at the berth,
  • the formulation plan can be updated again in consideration of the detailed ship allocation situation that appears in the results of the simulation by the inventory transition simulator on the ship allocation plan side and the ship operation status transition simulator. By this action, a great effect is obtained in improving the accuracy of the blending plan.
  • the usage amount (mixing ratio) of each brand obtained by the second blending plan creation device 300, the amount received, a stock transition graph, and various forms are displayed.
  • the operator evaluation unit 304 the operator evaluates the obtained blending plan from various viewpoints (for example, inventory transition, properties, etc.), and corrects the blending ratio and the like as necessary if the result is not satisfactory. . At that time, the weight of the objective function and the evaluation index are changed as necessary, and the target period / plan decision period for setting the mathematical model is changed. In addition, it is possible to set a mathematical model reflecting the operator's will, such as fixing the amount of use only for all or specified processes. Then, the blending plan is created again by the second blending plan creation device 300.
  • viewpoints for example, inventory transition, properties, etc.
  • FIG. 3 is a block diagram showing a basic configuration of the second formulation planning device.
  • the second blending plan creation apparatus 300 includes a simulator (including an inventory transition simulator 311 and a property simulator 312), a model setting unit (demand / supply balance model setting) that functions as a mathematical model setting unit in the present invention. Unit 313, property model setting unit 314), and a plan unit 315 functioning as an optimization calculation unit in the present invention, and further includes an input / output unit.
  • the inventory transition simulator 311 is a simulator for calculating the supply and demand state (inventory transition) of each raw material.
  • the property simulator 312 is a simulator that calculates properties after mixing raw materials. The inventory transition simulator 311 and the property simulator 312 work together to calculate the inventory transition of raw materials and the properties after mixing.
  • a mathematical model setting process is performed based on input data 316 including the following information: a plan creation period, a raw material arrival schedule, a raw material inventory status, a raw material property, and a raw material unit price.
  • LP Linear Programming
  • MIP Mated Integer Programming
  • QP Secondary Programming
  • a mathematical model representing supply / demand balance constraints is set by the supply / demand balance model setting unit 313
  • a mathematical model representing property constraints is set by the property model setting unit 314.
  • the planning unit 315 performs optimization calculation so as to create a blending plan by minimizing. Based on the result of this optimization calculation, calculation instructions for the inventory transition simulator 311 and the property simulator 312 are calculated. In response to this calculation instruction, the inventory transition simulator 311 simulates the inventory transition, and the property simulator 312 simulates the properties of the products and semi-finished products manufactured according to the plan.
  • properties such as coke (product) that is baked and hardened by mixing coal, and slab (semi-finished product) that is obtained by solidifying molten steel obtained by refining pig iron obtained by reducing iron ore are simulated.
  • calculation is not performed based on a predetermined rule as in the prior art, but calculation based on the result of optimization calculation performed by the planning unit 315.
  • the instruction is output to the inventory transition simulator 311 and the property simulator 312. For this reason, it is possible to reliably perform an optimal calculation instruction according to the event at that time.
  • a simulation by the inventory transition simulator 311 and the property simulator 312 is performed for a predetermined plan fixed period.
  • the planning start date is updated, and based on the final state of the plan finalization period before the update, that is, inventory transition and property information at the planning start date after the update, the new optimization period
  • a mathematical model representing inventory constraints is set by the supply / demand balance model setting unit 313, and a mathematical model representing property constraints is set by the property model setting unit 314, and is given to the planning unit 315.
  • the planning unit 315 executes optimization calculation.
  • the simulator the inventory transition simulator 311 and the property simulator 312
  • the model setting unit the supply and demand balance model setting unit 313, the property model setting unit 314.
  • the planning unit 315 are linked to create a blending plan. For this reason, the following effects are obtained. (1) A blending plan can be created without repeatedly executing a simulation. (2) Calculation time can be shortened by incorporating only important parts that have a large influence on the formulation plan creation into the planning unit 315, and (3) large-scale problems can be solved.
  • the outline of formulation planning includes the following calculation and adjustment processes: Supply and demand balance of raw materials (brands) at a plurality of steelworks (lifting ports) a to c To satisfy the required properties; and to minimize costs (raw material purchase and transportation costs); to satisfy the above conditions
  • a blending plan determine the usage (mixing ratio) and receipt of each brand A to N for each steelworks a to c.
  • the arrival schedule of the raw materials taken in by the input data fetching unit 351 includes the ship allocation plan (shipping port for each vessel, arrival date / time of arrival at the loading port, brand name, loading volume, arrival / departure date / time at arrival at the arrival / disembarkation port) , Information on the amount received, such as a plan for an item including a lifted brand and a lifted amount).
  • a plan for an item including a lifted brand and a lifted amount For example, in the ship allocation plan shown in FIG. 26, the operation schedule of each ship listed in the ship list as shown in FIG. 13 is set.
  • a plan is formulated for the items listed in the ship list, including items including loading / unloading date / time, brand, volume, landing / unloading date / time, brand / lift. ing.
  • the purchase status information indicating the stock status of raw materials, the properties of the raw materials, and the unit price of the raw materials is the same as in the case of the first blending plan creating apparatus 100 described in the first embodiment.
  • the ship allocation plan creation device 200 passes the ship allocation plan planned for the future to the second combination plan creation device 300 as input data. For this reason, it is not necessary to use the freight by brand / shipping port, and the freight by ship / shipping port / shipping port can be used.
  • the above-described input data fetching unit 351 and step S301 are examples of the data fetching unit of the second blending plan creating unit and processing by it in the present invention.
  • plan creation period setting unit 352 in FIG. 4, step S302 in FIG. 5 Set the period for creating a recipe. This creation period can be set as desired according to the planner's needs. Here, 10 days is planned as an example.
  • time accuracy setting unit 353 in FIG. 4, step S303 in FIG. 5 Set the time accuracy and simulation accuracy to create a recipe.
  • the time accuracy and the simulation accuracy can be arbitrarily set individually according to the planner's needs. For example, by making the precision fine in the first half of the planning period that requires fine planning accuracy, and by making the precision coarse in the second half of the sufficient planning period with a rough plan, it is efficient with sufficient precision and short calculation time. Planning is possible.
  • Optimization period setting (optimization period setting unit 354 in FIG. 4, step S304 in FIG. 5) Set the optimization period for creating a recipe.
  • This optimization period can be set to any target period individually as required by the planner.
  • the optimization period is 3 days throughout the planning period.
  • Supply / demand balance constraints of the composition plan are set in the mathematical model (supply / demand balance model setting unit 313 in FIG. 3, supply / demand balance model setting unit 356 in FIG. 4, step S306 in FIG. 5). Based on all or a part of the data fetched by the input data fetching unit 351, the mathematical model is set based on the supply-demand balance constraint with the set time accuracy for the set optimization period.
  • the stock amount of each brand is required to be equal to or greater than a value called a certain safety stock amount.
  • the constraint in this case is expressed as (Equation 4) above.
  • the stock quantity of each brand is determined from the inventory quantity of the previous day, the arrival quantity of the previous day, and the usage quantity of the previous day.
  • the constraint representing the relational expression in this case is expressed as the above (Formula 5).
  • the stock quantity on the current day is a value obtained by subtracting the use quantity on the current day from the value obtained by adding the stock quantity on the previous day and the quantity received (unloaded) on the current day.
  • the operator sets a target blending ratio and requests that a blending plan that realizes a blending ratio close to the given blending ratio is created.
  • the blending ratio is far from the operator's assumption, it is assumed that the assumed purchase amount cannot be satisfied, the purchase amount is exceeded, or the operation facility is unreasonably operated. For this reason, it is necessary to output a blending ratio close to the blending ratio given as a target.
  • the restrictions for realizing the above functions are shown below. That is, a value obtained by subtracting the target usage amount (target mixture ratio) (constant) from the brand usage amount is defined as a variable of the overflow amount from the usage target amount.
  • this overflow amount is added as an item of the objective function, and is minimized by optimization.
  • a value obtained by subtracting the use amount from the use target amount of the brand is defined as a variable of the shortage amount from the use target amount.
  • the plan is such that the usage amount and the usage target amount are close to each other, the smaller the shortage amount, the better. For the above reason, as described later, this shortage is added as an item of the objective function and is minimized by optimization.
  • Equation 7 the constraint equation representing the relationship between the usage amount, the usage target amount, the overflow amount, and the shortage amount of each brand is expressed as the above (Equation 7). That is, if overflow occurs, the overflow amount is subtracted from the usage amount, and if there is a shortage, the shortage amount is added to match the target usage amount.
  • a mathematical model is set using the property constraint of the blending plan (the property model setting unit 314 in FIG. 3, the property model setting unit 357 including the linearization unit 357a in FIG. 4, and steps S307 and S307a in FIG. 5). Based on the whole or part of the data fetched by the input data fetching unit 351, the mathematical model is set using the property constraints using the set optimization period and time accuracy.
  • the properties of raw materials used for the properties include, for example, the following: iron, SiO 2 , Al 2 O 3 , SiO 2 , etc.
  • the properties for preparing a coal blending plan include the following: CSR (strength after hot reaction), DI (coke strength), VM (volatile matter), expansion pressure, and the like. These properties must satisfy the required property constraints.
  • CSR stress after hot reaction
  • DI coke strength
  • VM volatile matter
  • expansion pressure and the like.
  • the formula f (xA, xB, xC,..., XN) included in the property model is linear with respect to the blending ratio as shown in (Formula 15) above.
  • SiO 2 component 40% proportion of the stock A is SiO 2 component is 1% stock A
  • 60% proportion of the stock B is, if the SiO 2 component is mixed with 2% condition, after mixing
  • the mathematical expression f (xA, xB, xC,..., XN) representing the properties may be nonlinear.
  • the linearizing unit 357a replaces the nonlinear mathematical expression f (xA, xB, xC,..., XN) with the linear mathematical expression f ′ (xA, xB, xC,. XN) to formulate the mathematical model.
  • the weighted average shown in the above is considered as a linear mathematical expression f ′ (xA, xB, xC,..., XN).
  • the weighted average is a value obtained by calculating the properties when a single brand is used 100% from a non-linear formula f (xA, xB, xC,..., XN), multiplying the blending ratio, and adding the used brands. It is.
  • a weighted average that is a linear mathematical expression f ′ (90, 0, 10,..., 0) is represented by the following expression.
  • f ′ (90, 0, 10,..., 0) 0.9 x f (100, 0, ..., 0) + 0.1 x f (0, 0, 100, ... 0)
  • the supply / demand balance model setting unit 356 (supply / demand balance model setting unit 313) and step S306, the property model setting unit 357 (properties model setting unit 314 in FIG. 3), and steps S307 and S307a described above are referred to in the present invention. It is a numerical formula model setting part of 2 combination plan preparation parts, and the example of a process by it.
  • Immobilization extraction processing (immobilization extraction processing unit 358 in FIG. 4, step S308 in FIG. 5)
  • items that are fixed that is, those that cannot be changed, are extracted from among the shipping port items, loading brands, loadings, landing ports, lifting brands, and lifting amounts, which are items of the ship allocation plan.
  • the “loading port” and “loading brand” are fixed for each dredger according to the conditions given in advance, the freight rate for each ship, each loading port, and each lifting port (see FIG. 15) is used.
  • these dredgers have been determined to be loaded with raw materials, by using the freight by ship, by port, by port, and when the steelworks that will receive the raw materials is determined by optimization, Accurate transportation cost calculation is possible.
  • the objective is to minimize costs (raw material purchase costs and transportation costs), and an example of an objective function J is shown in (Equation 40).
  • the purchase cost information and the transportation cost information set in step S308 are used.
  • Equation 40 is an example of an objective function, and other objective functions may be substituted or other objective functions may be added. For example, when it is necessary to bring the blending plan closer to the blending ratio close to the given target blending ratio, and it is necessary to create a blending plan in which the blending ratio of the previous day and the blending ratio of the next day do not greatly differ think of. In this case, an overflow amount, a deficiency amount from the target usage amount, and items for minimizing a difference between the usage amount on the day and the usage amount on the day before are added to the objective function.
  • the above-described blending plan solution unit 359 (planning unit 315) and step S309 are examples of the optimization calculation unit of the second blending plan creation unit referred to in the present invention and processing by it.
  • Formula model f ′ (xA, xB, xC,..., XN) including a linear formula if the formula model f (xA, xB, xC,..., XN) ⁇ S including a nonlinear formula is not satisfied.
  • ⁇ S ′ is adjusted (step S311 in FIG. 5). Specifically, the temporary lower limit S ′ is slightly increased.
  • step S402 the solution obtained by the optimization calculation using the mathematical model f ′ (xA, xB, xC,..., XN) ⁇ S ′ including a linear mathematical expression is a mathematical model f including a nonlinear mathematical expression. It is determined whether (xA, xB, xC,..., XN) ⁇ S is satisfied. That is, the result of the optimization calculation in step S401 (the usage amount (mixing ratio) of each brand A to N) is substituted into (Expression 14), and it is determined whether (Expression 14) is satisfied.
  • the inventory transition simulator 361 (inventory transition simulator 311) and step S312, and the property simulator 362 (property simulator 312) and step S313 described above are the simulator of the second blending plan creation unit referred to in the present invention and the processing performed thereby. It is an example.
  • Planning start date update (update unit 365 in FIG. 4, step S316 in FIG. 5) If the plan decision period determined at the time of execution of this step does not include the entire plan preparation period set in advance, the date and time immediately after the combination plan period determined in the combination plan is set as a new planning start date.
  • the planning start date that was initially 0 o'clock on the first day in the first loop is 0 o'clock on the second day, and the planning start that was originally 0 o'clock on the second day in the second loop is started. Update the day to 0:00 on the third day.
  • Output of formulation plan (output unit 366 in FIG. 4, step S317 in FIG. 5)
  • the formulation plan created as described above is displayed on the screen of the display unit 303 by the output unit 366 or is transmitted to an external device including the database 400.
  • the output unit 366 and step S317 described above are examples of the output unit of the second blending plan creation unit and the processing performed thereby in the present invention.
  • a mathematical model is set with the plan creation time accuracy for the predetermined optimization period, Solves based on the function, simulates inventory transition and mixed properties based on the solved solution, confirms the set plan finalization period from the formulation plan obtained from the simulation results, and finalizes the plan
  • a series of processes for determining a blending plan for a new planning target period is sequentially and repeatedly executed a predetermined number of times.
  • blending plan for the plan preparation period desired can be created. This makes it possible to optimize a blending plan that requires an arbitrary time accuracy at high speed and in detail, and to apply the obtained results to actual operations as they are.
  • the blending plan is created every certain period (for example, seasonal).
  • the property model may be nonlinear with respect to a plurality of properties ⁇ and ⁇ .
  • A means that the property constraint is satisfied (Equation 14 is satisfied), and B means that the property constraint is not satisfied.
  • Equation 14 is satisfied
  • B means that the property constraint is not satisfied.
  • the convergence calculation described in FIG. 10 is performed separately for each season and each property, the following problems occur. Specifically, the convergence calculation is performed for the property ⁇ in early April, the convergence calculation is performed for the property ⁇ , the convergence calculation is performed for the property ⁇ in late April, and the convergence calculation is performed for the property ⁇ . Doing so will take time for the calculation process.
  • a long-term plan such as an annual plan, a term plan, or a monthly plan is formulated as a blending plan (for example, usage (mixing ratio)).
  • a long-term blending plan is created in advance, the blending plan is used as a reference blending plan, and the shorter-term blending plan created by the first blending plan creation device 100 is more than a certain distance away from the reference blending plan. It is also important to avoid it.
  • a daily blending plan as a blending plan based on the term plan in the monthly plan.
  • the sum totaled for each brand and each day of the difference between the blending ratio (brand, day) and the standard blending ratio is minimized.
  • the plan may be created as a blending plan based on the annual plan.
  • the monthly plan when it is determined that the blending ratio (brand, month) is determined, the difference between the blending ratio (brand, month) and the standard blending ratio is summed for each brand and every month.
  • standard is produced based on the past performance, for example,
  • the production method may be what kind.
  • a long-term plan may be created in advance by a blending plan creation method to which the present invention is applied, and this may be used as a reference blending plan.
  • a blending plan for receiving and mixing multiple brands of raw materials and a ship allocation plan for transporting multiple brands of raw materials from multiple loading points to multiple landing sites can be created in a batch. It becomes possible. Moreover, in these plans, it becomes possible to minimize the transportation cost as a whole through the blending plan / ship allocation plan. Furthermore, a blending plan / shipping plan that can prevent out-of-stock more reliably than when creating a use plan with the first blending plan creation device 100 and creating a ship assignment plan using this use plan as input data is provided. Can be created.
  • the safety stock amount may not be cut for each individual brand as a constraint on the stock of each brand (Equation 24).
  • brands with similar properties multiple brands that share the same chemical properties within the specified range: brands that can be used even if they are replaced with each other
  • brands with similar properties can be used interchangeably.
  • the stock quantities of raw materials A and B are 50,000 tons and 0 tons, respectively, if the raw material A is 0 tons and B is 20,000 tons in the usage plan, out of stock will occur if only the B brand is considered.
  • the operation of using 20,000 tons of A instead of using 20,000 tons of the raw material B is performed. This can prevent out of stock.
  • grouping of brands using conditions related to physical or chemical properties of raw materials such as “the degree of coalification is 70% or less; greater than 70% and less than 90%; greater than 90%”. I do.
  • grouping the brands in this way and treating them as one makes it possible to reduce the number of variables and the amount of calculation.
  • an object is to realize creation of a blending plan / ship allocation plan that realizes the above operation.
  • the ship allocation plan creation device 200 does not treat the restrictions on the stock of each brand as if the safety stock amount is not cut for each individual brand, but groups brands having properties that can be substituted for each other. handle. That is, in the grouped brands, the stock constraint is created on the assumption that the stock quantity of the brand as a group does not cut the safety stock quantity as the group.
  • a blending plan for receiving and mixing multiple brands of raw materials and a ship allocation plan for transporting multiple brands of raw materials from multiple loading points to multiple landing sites can be created in a batch. It becomes possible. Moreover, in these plans, it becomes possible to minimize the transportation cost as a whole through the blending plan / ship allocation plan. Further, when a use plan is created by the first formulation plan creation device 100 and a brand having a property that can be substituted is considered as a group as input data relating to this use plan, a ship allocation plan that does not run out of stock is created. Compared to the above, it is possible to create a blending plan / ship allocation plan with lower transportation costs.
  • a use plan is created by the first blend plan creation device 100, and the use plan is considered as input data, a brand having an alternative property is considered as a group, and a ship allocation plan that does not run out of stock is created.
  • the embodiment to be created has been described.
  • the shipping plan created by the shipping plan creation device 200 is used as input data, and a blending plan is created by the second blending plan creation device 300. It is possible to create a blending plan.
  • the second blending plan creation device 300 can also create a blending plan without difficulty in property restrictions. Furthermore, in the 1st mixing
  • a combination plan that receives and mixes multiple brands of raw materials and a ship allocation plan that transports multiple brands of raw materials from multiple loading sites to multiple landing sites are linked together. It becomes possible to create. Moreover, in these plans, it becomes possible to minimize the transportation cost as a whole through the blending plan / ship allocation plan. Further, a use plan is created by the first blending plan creation device 100, and a blending plan / shipping plan with a lower transportation cost is created as compared with the case where a shipping plan is created using this use plan as input data. It becomes possible. Furthermore, it becomes possible to create a more accurate use plan by creating a use plan by the second blending plan creation device 300.
  • a recording medium for storing the program code for example, a flexible disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used.
  • the present invention further includes the following aspects. (1) When blending raw materials of multiple brands that have been transported and received from multiple suppliers to multiple suppliers by ship, and used at each supplier, the blending plan of the blended raw materials of the multiple brands, And a blending and dispatching plan creation system for creating a ship allocation plan for a ship that transports a plurality of blended raw materials from a plurality of suppliers that are loading sites to a plurality of destinations that are landing sites, First blending plan creation means for creating a blending plan for receiving and mixing a plurality of branded blending raw materials based on a take-up target amount preset for each supplier and each brand, and the first blending Based on the blending plan created by the plan creating means, a ship assignment plan creating means for creating a ship assignment plan for transporting a plurality of branded raw materials from a plurality of loading sites to a plurality of landing sites, and the ship assignment plan creating means
  • the ship allocation plan created by A blending and ship allocation plan creation system characterized by comprising database means for storing.
  • Ship funding source creation means for selecting a ship from the ship list and creating necessary ship funding sources, at least ship operation constraints created by the ship funding source creation means, supply and demand balance restrictions for compound raw materials at the landing site, and takeover target Formula simulator that builds a formula model that expresses a quantity constraint, and the simulator that performs optimization calculation based on an objective function that is built at least with respect to transportation costs, using the formula model built by the formula model construction means Any
  • the ship allocation plan creating means is a ship list in which ships with different planned raw material usage amounts, take-up target quantities, and contract types according to the blending plan created by the first blending plan creating means are listed.
  • the operation status of the vessels listed in the vessel list the inventory status of the compounded raw materials handled by grouping the brands with similar properties, the purchase cost information of the compounded raw materials, and the vessels listed in the vessel list
  • Data capture means for capturing data including transportation cost information when used, inventory transition simulator for calculating inventory transition of compound raw materials handled by grouping brands with similar properties, and ship for calculating transition of ship operation status From the ship list based on the simulator configured by the operation status transition simulator and the vessel operation status
  • a selection of vessels and a vessel funding creation means that creates the necessary vessel funding, and a combination raw material that handles at least the ship operation restrictions created by the vessel funding creation means and the brands with similar properties at the landing site.
  • Second blending plan creation means for creating a blending plan for receiving and mixing a plurality of branded blending raw materials based on the dispatching plan created by the dispatching plan creation means, and the second blending
  • the blending / shipping plan creation system according to any one of (1) to (6), further comprising database means for storing the blending plan created by the plan creation means.
  • the second blending plan creation means includes the arrival schedule of the blended raw materials, the stock status of the blended raw materials, the properties of the blended raw materials, and the purchase cost information of the blended raw materials.
  • a blending plan for receiving and mixing a plurality of branded raw materials and a ship allocation plan for transporting a plurality of branded raw materials from a plurality of loading sites to a plurality of landing sites are linked to each other and collectively.
  • the plan for minimizing transportation costs including whether or not to hire a ship that determines the type of dredger contract (continuous cruise ship, irregular ship, spot ship), and fleet composition. Planning is possible.

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Abstract

A system for making an allocation to ships which transport a plurality of brands of composition raw material from a plurality of loading places to a plurality of unloading places, and a composition plan for mixing the received composition raw materials at the unloading places, respectively, is provided with a first composition plan making section which makes a first composition plan based on the preset target quantity of transaction of the composition raw materials for each loading place and band, a ship allocation making section which makes an allocation to ships based on the first composition plan thus made, and a database section which stores the allocation to ship thus made.

Description

配合及び配船計画作成システム、方法及びプログラムFormulation and dispatch plan creation system, method and program
　本発明は、複数銘柄の配合原材料（以下、単に原材料とも称する）を入荷して混合する配合計画及び複数銘柄の原材料を複数の積地から複数の揚地に輸送する配船計画を作成するのに好適な配合及び配船計画作成システム、方法及びプログラムに関する。
　本願は、２００８年１１月２１日に、日本に出願された特願２００８－２９８５９６号に基づき優先権を主張し、その内容をここに援用する。 The present invention creates a blending plan for receiving and mixing a plurality of branded raw materials (hereinafter also simply referred to as raw materials) and a ship allocation plan for transporting a plurality of branded raw materials from a plurality of loading sites to a plurality of landing sites. The present invention relates to a composition, a ship allocation plan creation system, a method and a program suitable for the above.
This application claims priority based on Japanese Patent Application No. 2008-298596 filed in Japan on November 21, 2008, the contents of which are incorporated herein by reference.
　鉄鋼を始めとする多くの産業においては、契約した購入目標量に沿い購入した種々の性状を有する多種類の原材料を混合して、この混合後の原材料を使用して鉄鋼等の製品、半製品を製造する。この際、鉄鋼等の製品、半製品の性状を一定範囲内に納めつつ生産量を満足し、かつ、各原材料が製造現場で在庫切れを発生させないように、輸送計画（船の場合は配船計画と呼ぶ）及び配合計画を立てることが求められている。そして、配船計画及び配合計画を作成するに際しては、費用を重要な指標として計画の良し悪しが判断される。このため、輸送費用（船を雇う費用であるフレートと、船が港で契約期間以上に停泊した場合に支払う滞船料）、及び原材料の購入費用のミニマム化（最小化）が求められる。 In many industries, including steel, many types of raw materials with various properties purchased in accordance with the contracted purchase target amount are mixed, and the mixed raw materials are used to produce steel and other products and semi-finished products. Manufacturing. At this time, a transportation plan (allocation in the case of a ship) so that the production volume is satisfied while keeping the properties of steel and other products and semi-finished products within a certain range, and each raw material does not run out of stock at the manufacturing site. Called a plan) and a formulation plan. Then, when preparing a ship allocation plan and a composition plan, whether the plan is good or bad is judged by using the cost as an important index. For this reason, it is required to minimize (minimize) transportation costs (freight, which is the cost of hiring a ship, and berthing fees paid when the ship is anchored at the port for longer than the contract period), as well as raw material purchase costs.
　しかし、原材料を荷揚げ（入荷）する船舶の配船計画が定まらないと、揚港に荷揚げされる原材料の銘柄と量が決定されない。この結果、原材料を荷揚げされた製鉄所で、どの銘柄をどれだけの量使えるかを決定できない、つまり配合計画を作成できない。一方、船舶の配船計画を作成するためには、原材料を荷揚げする揚港である製鉄所で、どの銘柄をどれだけの量使うかが決まっている必要がある。このように、配合計画と配船計画とはお互いに深く相関し合っているため、そのいずれかを独立して計画することが非常に難しい。 However, the brand and quantity of the raw material to be unloaded at the unloading port cannot be determined unless the ship allocation plan for the ship that unloads (receives) the raw material is determined. As a result, it is impossible to determine how much of each brand can be used at the steelworks where the raw materials are unloaded, that is, it is impossible to create a recipe. On the other hand, in order to create a ship allocation plan, it is necessary to decide which brands and how much will be used at the steelworks where the raw materials are unloaded. As described above, since the blending plan and the ship allocation plan are deeply correlated with each other, it is very difficult to plan one of them independently.
　関連する技術として、特許文献１に開示の原料輸送配船計画用推論装置では、原材料の使用計画及び原料船の年間稼動計画を既知データとして読み込んだ後に、在庫切れを起こしそうな銘柄を優先的に船と紐付ける。この工程を繰り返すことで配船計画が作成される。 As a related technology, in the reasoning device for raw material transportation and shipping planning disclosed in Patent Document 1, a material use plan and an annual operation plan of a raw material ship are read as known data, and then a brand that is likely to run out of stock is given priority. Tie to the ship. By repeating this process, a ship allocation plan is created.
　また、特許文献２に開示の物流計画作成装置では、シミュレート部で制約条件に基づいて各輸送手段の運行をシミュレートし、原料在庫量推移算出部で上記シミュレート結果に基づいて原料銘柄毎の在庫量の推移を算出する。この計算の結果が評価値算出部で評価される。 Moreover, in the physical distribution plan creation apparatus disclosed in Patent Document 2, the simulation unit simulates the operation of each means of transportation based on the constraint condition, and the raw material inventory amount transition calculation unit calculates each material brand based on the simulation result. The change of the stock quantity of The result of this calculation is evaluated by the evaluation value calculation unit.
日本国特開平８－２７２４０２号公報Japanese Laid-Open Patent Publication No. 8-272402 日本国特開平１１－３１０３１３号公報Japanese Unexamined Patent Publication No. 11-310313
　しかしながら、特許文献１及び特許文献２はいずれも、原材料の使用計画及び原料船の年間稼動計画を既知データとして読み込んで配船計画を作成する。つまり、配合計画と配船計画とを相互に連係させて作成することは考慮されていない。 However, both Patent Document 1 and Patent Document 2 read the raw material usage plan and the raw material ship annual operation plan as known data, and create a ship allocation plan. That is, it is not taken into consideration that the formulation plan and the shipping plan are linked to each other.
　また、現実の輸送に際しては、異なる種別の傭船契約に基づいて運用される船舶、例えば連続航海船、不定期船、スポット船が使用されるが、特許文献１、２ではそういった船舶の種類までも含めた輸送費用のミニマム化は、考慮されていない。 In actual transportation, ships operated based on different types of charter contracts, for example, continuous voyage ships, irregular ships, and spot ships are used. Minimization of the transportation cost is not considered.
　更に、連続航海船及び不定期船といった契約では、対象船舶を必ず（最優先で）配船をする必要がある。一方、スポット船は、引取目標量、在庫状況に応じて適切に雇う船の大きさ、船数を決定する必要がある。しかし、特許文献１、２ではそういった傭船契約種別を考慮した船の船団構成までを考慮した送費用のミニマム化は、行われていない。つまり、例えば、１つの連続航海船と最大積載量７５０００トン（ｔｏｎ、ｔ）の２つのスポット船で配船するのが良いのか、或いは１つの連続航海船と最大積載量５００００トンの３つのスポット船で配船するのが良いのか、などの選択肢を考慮した配船計画の方法が、実際の業務では必要となる。雇う船の最大積載量、航路によって船の輸送費用は大きく変わってくるが、これらを考慮した配船は、上記文献では行われていない。 Furthermore, in contracts such as continuous cruise ships and irregular ships, it is necessary to dispatch the target ship (with the highest priority). On the other hand, for spot ships, it is necessary to determine the size of the ship to be hired and the number of ships appropriately according to the take-up target amount and inventory status. However, Patent Documents 1 and 2 do not minimize the transportation cost considering the fleet composition of the ship considering the charter contract type. In other words, for example, it is better to ship with one continuous cruise ship and two spot ships with a maximum load of 75,000 tons (ton, t), or three spots with one continuous cruise ship and a maximum load of 50000 tons. In actual work, it is necessary to have a ship planning method that considers options such as whether to ship by ship. Ship transportation costs vary greatly depending on the maximum loading capacity and the route of the ship to be hired, but ship allocation considering these factors is not performed in the above document.
　更に、配合計画で輸送費用を考慮せずに計画を作成した場合には、必要以上に輸送費用が高い原材料を使用する配合計画が作成される畏れがある。このような計画方法の場合、配合計画決定後にどのように輸送を工夫しても、輸送費用を最適化することは困難である。例えば、性状がほぼ同一の原材料Ｘ，Ｙがあり、揚港（製鉄所）Ａ，Ｂでは原材料Ｘ，Ｙどちらでの使用も可能な場合を考慮する。揚港Ａに原材料Ｘを輸送する費用が２０＄／トン、原材料Ｙを輸送する費用４０＄／トン、揚港Ｂに原材料Ｘを輸送する費用が４０＄／トン、原材料Ｙを輸送する費用２０＄／トンであるとする。この場合、本来揚港Ａで原材料Ｘ、揚港Ｂで原材料Ｙを使用する計画を立てる方が輸送費用の観点からより有利な計画である。しかし、輸送費用が考慮されていない計画方法を用いた場合、揚港Ａで原材料Ｙ、揚港Ｂで原材料Ｘを使用する計画が作成される畏れがある。
　　加えて、実操業においては、使用予定銘柄の在庫状況が厳しい場合には、性状の近い銘柄（性状が規定した範囲に含まれる銘柄；一定の化学性質を共通して備える銘柄；互いに置き換えても使用可能な銘柄）を代替として使用することで、在庫切れの抑止が行われている。また、この代替使用を積極的に行うことで、フレートが高い船でしか輸送できない銘柄に変わり、性状の近い銘柄（性状が規定した範囲に含まれる銘柄）でフレートのより安い船で手配できる銘柄を輸送することで、輸送費用の削減を行っている。 Furthermore, when a plan is created without considering the transportation cost in the blending plan, there is a possibility that a blending plan using raw materials having a transportation cost higher than necessary may be created. In the case of such a planning method, it is difficult to optimize the transportation cost no matter how the transportation is devised after the formulation plan is determined. For example, a case is considered in which there are raw materials X and Y having substantially the same properties, and the use of both raw materials X and Y is possible at Yanggang (ironworks) A and B. The cost of transporting the raw material X to the unloading port A is 20 $ / ton, the cost of transporting the raw material Y is 40 $ / ton, the cost of transporting the raw material X to the unloading port B is 40 $ / ton, and the cost of transporting the raw material Y is 20 Suppose that it is $ / ton. In this case, it is more advantageous from the viewpoint of transportation cost to make a plan to use the raw material X at the unloading port A and the raw material Y at the unloading port B. However, when a planning method that does not consider transportation costs is used, there is a possibility that a plan for using the raw material Y at the unloading port A and the raw material X at the unloading port B may be created.
In addition, in actual operation, if the stock status of the stock to be used is severe, stocks with similar properties (stocks included in the range specified by the properties; stocks with a certain chemical property in common; Stocks that can be used) are used as alternatives to prevent out of stock. In addition, by actively using this alternative, it will be changed to a brand that can only be transported by a ship with a high freight rate, and a brand that can be arranged with a cheaper freight brand with a brand with similar properties (brands that are included in the specified range). Transportation costs are reduced by transporting
　このように、費用を考慮する場合には、配合原材料の購入費用はもちろん、その輸送費用も考慮して、計画を作成することが求められる。しかし、特許文献１及び特許文献２はいずれも、原材料の使用計画の段階において、上記事情までは考慮していない。 In this way, when considering costs, it is necessary to create a plan that takes into account the transportation costs as well as the purchase costs of the raw materials. However, neither Patent Document 1 nor Patent Document 2 considers the above situation at the stage of planning to use raw materials.
　本発明は以上のような状況に鑑みてなされたものであり、複数銘柄の配合原材料を入荷して混合する配合計画、及び複数銘柄の原材料を複数の積地から複数の揚地に輸送する配船計画を、相互に連係させて一括して作成できるようにすることを一つの目的とする。また、船舶の契約種類までも含めて輸送費用のミニマム化が考慮できるようにすることを別の一つの目的とする。
　更に、性状の近い銘柄（性状が規定した範囲に含まれる銘柄）を考慮することで、銘柄毎に個別に考慮するのに比べて更なる在庫切れの抑制と輸送費用のミニマム化を可能にすることを目的とする。 The present invention has been made in view of the situation as described above, and is a blending plan for receiving and mixing a plurality of branded raw materials, and an arrangement for transporting a plurality of branded raw materials from a plurality of loading sites to a plurality of landing sites. One purpose is to enable ship plans to be linked together and created together. Another object is to make it possible to consider the minimization of transportation costs including the contract types of ships.
Furthermore, by considering brands with similar properties (brands that are included in the range specified by the properties), it is possible to further reduce stockout and minimize transportation costs compared to individual brands. For the purpose.
（１）　上記目的を達成するための、本発明の一態様にかかるシステムは、複数の銘柄の配合原材料を、複数の積地から複数の揚地に輸送する配船計画と；入荷した前記配合原材料を、それぞれの前記揚地で混合する配合計画と；を作成する配合及び配船計画作成システムであって：前記積地毎及び前記銘柄毎に予め設定された前記配合原材料の引取目標量に基づいて、第１の配合計画を作成する第１の配合計画作成部と；作成された前記第１の配合計画に基づいて、前記配船計画を作成する配船計画作成部と；作成された前記配船計画を格納するデータベース部と；を備える配合及び配船計画作成システムである。
（２）　上記（１）の配合及び配船計画作成システムでは、前記配合計画の作成と、前記配船計画の作成と、において、前記引取目標量および前記配合原材料の在庫状況に関して共通の制約条件が用いられてもよい。
（３）　上記（１）の配合及び配船計画作成システムにおいて、前記第１の配合計画作成部は：前記配合原材料の前記引取目標量、前記配合原材料の在庫状況、前記配合原材料の性状、前記配合原材料の購入費用、及び、前記配合原材料の輸送費用を含むデータを取り込むデータ取込み部と；前記配合原材料の需給バランス制約、及び、前記配合原材料の混合後の性状制約を表す数式モデルをそれぞれ設定する数式モデル設定部と；設定された前記数式モデルを用いて、前記購入費用及び前記輸送費用に関して予め構築された目的関数に基づいて最適化計算を行う最適化計算部と；前記最適化計算の結果に基づいて動作し、前記配合原材料の需給状態の推移、及び、前記配合原材料の混合後の前記性状の推移をシミュレートするシミュレータと；前記シミュレータによるシミュレーション結果である配合計画を出力する出力部と；を備えてもよい。
（４）　上記（３）の配合及び配船計画作成システムにおいて、前記最適化計算部が、前記配合原材料の入荷量と前記引取目標量との関係に関して予め構築された目的関数に更に基づいて最適化計算を行ってもよい。
（５）　上記（３）または（４）の配合及び配船計画作成システムにおいて、前記データ取込み部により取り込まれる前記輸送費用には；船舶別・積港別・揚港別のフレートの情報と；前記銘柄別・揚港別フレートの情報と；が含まれてもよい。
（６）　上記（１）の配合及び配船計画作成システムにおいて、前記第１の配合計画作成部は、更に、作成された前記配合計画に従った前記配合原材料の使用予定量を算出し、前記配船計画作成部は：前記配合原材料の前記使用予定量、前記配合原材料の前記引取目標量、前記配合原材料の在庫状況、前記配合原材料の購入費用、複数の種別の傭船契約に基づいて運用される複数の船舶がリストアップされた船舶リスト、各々の前記船舶の運航状況、及び、輸送費用、を含むデータを取り込むデータ取込み部と；前記運航状況に基づいて前記船舶リストから配船対象の候補となる前記船舶を選択し、船舶財源リストを作成する船舶財源リスト作成部と；前記船舶財源リストに含まれる前記船舶の運航制約、前記揚地での前記配合原材料の需給バランス制約、及び、引取目標量制約を表す数式モデルを設定する数式モデル設定部と；設定された前記数式モデルを用いて、前記輸送費用に関して予め構築された目的関数に基づいて最適化計算を行う最適化計算部と；前記在庫状況の推移をシミュレートする在庫推移シミュレータと、前記運航状況の推移をシミュレートする船舶運航状況推移シミュレータと、を含み、前記最適化計算の結果に基づいて動作する、シミュレータと；前記シミュレータによるシミュレーション結果である前記配船計画を出力する出力部と；を備えてもよい。
（７）　上記（１）の配合及び配船計画作成システムにおいて、前記第１の配合計画作成部は、更に、作成された前記配合計画に従った前記配合原材料の使用予定量を算出し、前記配船計画作成部は：前記配合原材料の前記使用予定量、前記配合原材料の前記引取目標量、性状が規定した範囲に含まれる複数の前記銘柄の前記配合原材料がグループ化されて取り扱われたグループ化配合原材料の在庫状況、前記配合原材料の購入費用、複数の種別の傭船契約に基づいて運用される複数の船舶がリストアップされた船舶リスト、各々の前記船舶の運航状況、及び、輸送費用、を含むデータを取り込むデータ取込み部と；前記運航状況に基づいて前記船舶リストから配船対象の候補となる前記船舶を選択し、船舶財源リストを作成する船舶財源リスト作成部と；前記船舶財源リストに含まれる前記船舶の運航制約、性状が規定した範囲に含まれる複数の前記銘柄の前記配合原材料がグループ化されて取り扱われた前記グループ化配合原材料の需給バランス制約、及び、引取目標量制約を表す数式モデルを設定する数式モデル設定部と；設定された前記数式モデルを用いて、前記輸送費用に関して予め構築された目的関数に基づいて最適化計算を行う最適化計算部と；性状が規定した範囲に含まれる複数の前記銘柄の前記配合原材料がグループ化されて取り扱われた前記グループ化配合原材料の前記在庫状況の推移をシミュレートする在庫推移シミュレータと、前記運航状況の推移をシミュレートする船舶運航状況推移シミュレータと、を含み、前記最適化計算の結果に基づいて動作する、シミュレータと；前記シミュレータによるシミュレーション結果である前記配船計画を出力する出力部と；を備えてもよい。
（８）　上記（１）（３）（７）のいずれかの配合及び配船計画作成システムは、　作成された前記配船計画に基づいて、第２の配合計画を作成する第２の配合計画作成部を更に備えてもよく、前記データベース部は前記第２の配合計画を更に格納してもよい。
（９）　上記（８）の配合及び配船計画作成システムにおいて、前記第２の配合計画作成部は：作成された前記配船計画に基づく前記配合原材料の入荷予定、前記配合原材料の在庫状況、前記配合原材料の性状、前記配合原材料の購入費用、及び、前記配合原材料の輸送費用を含むデータを取り込む第２のデータ取込み部と；前記入荷予定を用い、前記配合原材料の需給バランス制約、及び、前記配合原材料の混合後の性状制約を表す数式モデルをそれぞれ設定する第２の数式モデル設定部と；設定された前記数式モデルを用いて、前記購入費用及び前記輸送費用に関して予め構築された目的関数に基づいて最適化計算を行う第２の最適化計算部と；前記最適化計算の結果に基づいて動作し、前記配合原材料の需給状態の推移、及び、前記配合原材料の混合後の前記性状の推移をシミュレートする第２のシミュレータと；前記シミュレータによるシミュレーション結果である配合計画を出力する第２の出力部と；を備えてもよい。
（１０）　上記（９）の配合及び配船計画作成システムでは、前記第１の配合計画の前記需給バランス制約と、前記第２の配合計画の前記需給バランス制約と、において、需要に関して同一の制約条件を設定し；前記第１の配合計画の前記需給バランス制約において、供給に関して、引取目標量を制約条件として設定し；前記第２の配合計画の前記需給バランス制約において、供給に関して、船舶による原材料の入荷量を制約条件として設定してもよい。
（１１）　上記（９）の配合及び配船計画作成システムでは、前記第１の配合計画の前記目的関数において、銘柄別・揚港別見做しフレートを用い；前記第２の配合計画の前記目的関数において、船舶別・積港別・揚港別フレートを用いてもよい。
（１２）　上記（１）または（３）の配合及び配船計画作成システムでは、作成された前記配合計画において、性状が規定した範囲に含まれる複数の前記銘柄の前記配合原材料がグループ化されて取り扱われてもよい。
（１３）　上記目的を達成するための、本発明の別の一態様にかかる方法は、複数の銘柄の配合原材料を、複数の積地から複数の揚地に輸送する配船計画と；入荷した前記配合原材料を、それぞれの前記揚地で混合する配合計画と；を作成する配合及び配船計画作成方法であって：前記積地毎及び前記銘柄毎に予め設定された前記配合原材料の引取目標量に基づいて、第１の配合計画を作成する第１の配合計画作成工程と；作成された前記第１の配合計画に基づいて、前記配船計画を作成する配船計画作成工程と；作成された前記配船計画をデータベースに格納するデータベース格納工程と；を備える。
（１４）　上記目的を達成するための、本発明の別の一態様にかかるプログラムは、上記（１）の配合及び配船計画作成システムの各部分としてコンピュータを機能させるためのプログラムである。 (1) In order to achieve the above object, a system according to an aspect of the present invention includes a ship allocation plan for transporting a plurality of brands of blended raw materials from a plurality of loading sites to a plurality of landing sites; A blending and ship allocation plan creation system that mixes raw materials at each of the landings; and: a pre-set target amount of the blended raw materials set for each loading place and each brand. A first blending plan creation unit that creates a first blending plan; and a shipping plan creation unit that creates the shipping plan based on the created first blending plan; A blending and shipping plan creation system comprising: a database unit for storing the shipping plan.
(2) In the composition and ship allocation plan creation system according to (1) above, common constraint conditions regarding the take-up target amount and the stock status of the composition raw materials in the preparation of the composition plan and the creation of the ship allocation plan May be used.
(3) In the blending and shipping plan creation system of (1) above, the first blending plan creation unit includes: the take-up target amount of the blended raw materials, the inventory status of the blended raw materials, the properties of the blended raw materials, A data capture unit that captures data including the purchase cost of the blended raw material and the transportation cost of the blended raw material; a mathematical expression model representing the supply / demand balance constraint of the blended raw material and the property constraint after mixing of the blended raw material A mathematical model setting unit that performs optimization using the mathematical model that has been set, and performs an optimization calculation based on an objective function that is constructed in advance with respect to the purchase cost and the transportation cost; A simulator that operates based on the results and simulates the transition of the supply and demand state of the blended raw materials and the transition of the properties after mixing of the blended raw materials; An output unit that outputs a blending plan that is a simulation result of the simulator.
(4) In the composition and ship allocation plan creation system of (3) above, the optimization calculation unit is further optimized based on an objective function constructed in advance with respect to the relationship between the amount of the composition raw material received and the take-up target amount Calculation may be performed.
(5) In the above composition (3) or (4) and ship planning plan creation system, the transportation cost captured by the data capturing unit includes: freight information for each ship, each port, and each port. And information on the freight by brand / shipping port may be included.
(6) In the blending and shipping plan creation system according to (1), the first blending plan creation unit further calculates a planned use amount of the blended raw materials according to the created blending plan, The ship allocation plan creation unit is operated based on the planned use amount of the mixed raw material, the take-up target amount of the mixed raw material, the inventory status of the mixed raw material, the purchase cost of the mixed raw material, and a plurality of types of chartering contracts. A data acquisition unit that captures data including a ship list in which a plurality of ships are listed, an operation status of each of the ships, and a transportation cost; a candidate for dispatch from the ship list based on the operation status; A ship fund list creation unit that selects the ship to be created and creates a ship fund list; operation restrictions of the ship included in the ship fund list, supply and demand of the compound raw material at the landing A mathematical expression model setting unit for setting a mathematical expression model representing a lance constraint and a take-up target amount restriction; and using the set mathematical expression model, an optimization calculation is performed on the basis of an objective function preliminarily constructed for the transportation cost An optimization calculation unit; an inventory transition simulator that simulates the transition of the inventory status; and a ship operation status transition simulator that simulates the transition of the operation status, and operates based on the result of the optimization calculation A simulator; and an output unit that outputs the ship allocation plan as a simulation result by the simulator.
(7) In the blending and shipping plan creation system according to (1), the first blending plan creation unit further calculates a planned use amount of the blended raw materials according to the created blending plan, The ship allocation plan creation unit is: a group in which the combination raw materials of the plurality of brands included in the range defined by the planned usage amount of the mixed raw materials, the take-up target amount of the mixed raw materials, and properties are handled as a group Stock status of chemical compound raw materials, purchase cost of the compound raw materials, ship list in which a plurality of ships operated based on a plurality of types of chartering contracts are listed, operation status of each of the ships, and transportation costs, A data capturing unit that captures data including a ship financial resource list that selects a ship that is a candidate for dispatch from the ship list based on the operational status and creates a ship financial resource list The supply and demand balance of the grouped compounded raw materials in which the compounded raw materials of a plurality of the brands included in the range defined by the operational restrictions and properties of the ship included in the ship funding list are grouped and handled A mathematical formula model setting unit for setting a mathematical model representing constraints and a target amount restriction for pick-up; and an optimization that uses the mathematical formula that has been set to perform optimization calculation based on an objective function that is constructed in advance with respect to the transportation cost A stock transition simulator that simulates a transition of the stock status of the grouped blended raw materials that are handled by grouping the blended raw materials of a plurality of the brands included in the range defined by the properties; A ship operation status transition simulator that simulates the transition of the operation status, and operates based on the result of the optimization calculation, Emulator and; an output unit which outputs the Sailing planning is the simulation result by the simulator; may comprise.
(8) The combination and ship allocation plan creation system according to any one of (1), (3), and (7) above, a second combination plan that creates a second combination plan based on the prepared ship allocation plan A creation unit may be further included, and the database unit may further store the second formulation plan.
(9) In the blending and shipping plan creation system of (8) above, the second blending plan creation unit includes: an arrival schedule of the blended raw materials based on the created ship dispatch plan, an inventory status of the blended raw materials, A second data capturing unit that captures data including the properties of the blended raw materials, the purchase costs of the blended raw materials, and the transportation costs of the blended raw materials; and the supply / demand balance constraint of the blended raw materials using the arrival schedule; A second mathematical model setting unit that respectively sets a mathematical model that represents a property constraint after mixing of the blended raw materials; and an objective function that is constructed in advance with respect to the purchase cost and the transportation cost by using the set mathematical model A second optimization calculation unit for performing an optimization calculation based on the above; a change in the supply and demand state of the compounding raw material that operates based on the result of the optimization calculation; and the compounding A second simulator that simulates the transition of the properties after mixing the raw materials; and a second output unit that outputs a blending plan that is a simulation result of the simulator.
(10) In the composition and ship allocation plan creation system of (9) above, the same constraint regarding demand in the supply / demand balance constraint of the first composition plan and the supply / demand balance constraint of the second composition plan A condition is set; a supply target amount is set as a constraint condition for supply in the supply-demand balance constraint of the first formulation plan; a raw material by a ship regarding supply in the supply-demand balance constraint of the second formulation plan May be set as a constraint condition.
(11) In the composition and ship allocation plan creation system of (9) above, in the objective function of the first composition plan, a freight rate classified by brand / shipping port is used; In the objective function, freight by ship, by port, and by port.
(12) In the blending and ship allocation plan creation system according to (1) or (3) above, the blended raw materials of a plurality of the brands included in a range defined by the properties are grouped in the created blending plan. May be handled.
(13) A method according to another aspect of the present invention for achieving the above object includes a ship allocation plan for transporting a plurality of branded raw materials from a plurality of loading sites to a plurality of landing sites; A blending plan for mixing the blended raw materials at each of the landings; and a blending / shipping plan creation method for creating: a collection target of the blended raw materials set in advance for each loading place and each brand A first blending plan creation step for creating a first blending plan based on the quantity; a shipping plan creation step for creating the ship assignment plan based on the created first blending plan; A database storage step of storing the ship allocation plan in a database.
(14) A program according to another aspect of the present invention for achieving the above object is a program for causing a computer to function as each part of the blending and ship planning plan creation system of (1) above.
　本発明の各態様によれば、複数銘柄の配合原材料を入荷して混合する配合計画、及び複数銘柄の原材料を複数の積地から複数の揚地に輸送する配船計画を、相互に連係させ、一括して作成することができる。特に、傭船契約の種別の異なる船舶を考慮して、輸送費用に関して構築された目的関数を用意し、この目的関数に基づいて最適化計算を行うことができる。このことにより、船舶の傭船契約の種類（連続航海船、不定期船、スポット船）、船団の構成を決める船舶を雇うか、雇わないか（船財源リストの作成）、までも含めた、輸送費用のミニマム化のための計画立案が可能になる。更には、配合計画段階においても、輸送費用をミニマム化することを考慮した計画の作成が可能となる。
加えて、性状の近い銘柄（性状が規定した範囲に含まれる銘柄）を考慮することで、銘柄毎に個別に考慮するのに比べて更なる在庫切れの抑制と輸送費用のミニマム化のための最適化が可能になる。 According to each aspect of the present invention, a blending plan for receiving and mixing a plurality of branded raw materials and a ship allocation plan for transporting a plurality of branded raw materials from a plurality of loading sites to a plurality of landing sites are linked to each other. Can be created in batch. In particular, it is possible to prepare an objective function constructed with respect to transportation costs in consideration of ships with different types of chartering contracts, and perform optimization calculation based on this objective function. In this way, transportation including the type of chartering contract of the ship (continuous voyage ship, irregular ship, spot ship), whether or not to hire a ship that determines the composition of the fleet (preparation of ship funding list), etc. It is possible to plan for minimizing costs. Furthermore, it is possible to create a plan in consideration of minimizing the transportation cost even at the formulation planning stage.
In addition, by considering brands with similar properties (brands included in the range specified by the properties), it is possible to further reduce stockout and minimize transportation costs compared to individual brands. Optimization is possible.
本発明の各実施形態に係る配合及び配船計画作成システムの概略構成を示す図である。It is a figure which shows schematic structure of the mixing | blending and ship allocation plan preparation system which concern on each embodiment of this invention. 第１の、または第２の配合計画作成装置を含むシステム構成例を示す図である。It is a figure which shows the system structural example containing a 1st or 2nd mixing | blending plan preparation apparatus. 第１の、または第２の配合計画作成装置の基本的な構成を示すブロック図である。It is a block diagram which shows the basic composition of the 1st or 2nd mixing | blending plan preparation apparatus. 第１の、または第２の配合計画作成装置の構成を示すブロック図である。It is a block diagram which shows the structure of the 1st or 2nd mixing | blending plan preparation apparatus. 第１の、または第２の配合計画作成装置による配合計画作成処理を説明するためのフローチャートである。It is a flowchart for demonstrating the mixing | blending plan preparation process by the 1st or 2nd mixing | blending plan preparation apparatus. 配合計画作成の概要を説明するための図である。It is a figure for demonstrating the outline | summary of mixing | blending plan preparation. 利用するフレートを設定するテーブルの例を示す図である。It is a figure which shows the example of the table which sets the freight to utilize. 入荷量が引取目標量から一定幅以上離れないという制約を説明するための図である。It is a figure for demonstrating the restriction | limiting that an arrival amount does not leave | separate a fixed width or more from a taking-up target amount. 入荷量が引取目標量から一定幅以上離れないという制約を説明するための図である。It is a figure for demonstrating the restriction | limiting that an arrival amount does not leave | separate a fixed width or more from a taking-up target amount. 配合計画作成の手順を説明するための図である。It is a figure for demonstrating the procedure of mixing | blending plan preparation. 非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に代えて線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）を導入したときの処理を示すフローチャートである。When a linear mathematical expression f ′ (x A , x B , x C ,..., X N ) is introduced instead of the nonlinear mathematical expression f (x A , x B , x C ,..., X N ) It is a flowchart which shows the process of. 配合計画を旬毎に作成した例を示す図である。It is a figure which shows the example which created the mixing | blending plan every season. 配船計画作成装置による配船計画作成処理を説明するためのフローチャートである。It is a flowchart for demonstrating the ship allocation plan preparation process by a ship assignment plan preparation apparatus. 取り込みデータのうちの船舶リストを説明するための図である。It is a figure for demonstrating the ship list | wrist among capture data. 取り込みデータのうちの船舶運航状況を説明するための図である。It is a figure for demonstrating the ship operation condition of acquisition data. 取り込みデータのうちのフレートリストを説明するための図である。It is a figure for demonstrating the freight list of capture data. 取り込みデータのうちの滞船料のリストを説明するための図である。It is a figure for demonstrating the list of the berthing charges among capture data. 船舶の選択処理を説明するためのフローチャートである。It is a flowchart for demonstrating the selection process of a ship. 抽出した連続航海船について計画作成期間における積地と揚地の組み合わせのパターンを作成している様子を示す図である。It is a figure which shows a mode that the pattern of the combination of a loading place and a landing place in the plan preparation period is created about the extracted continuous voyage ship. スポット船で補うべき原材料輸送量を説明するための図である。It is a figure for demonstrating the raw material transport amount which should be supplemented with a spot ship. スポット船の航路リストを説明するための図である。It is a figure for demonstrating the route list of a spot ship. 時刻と在庫量との関係を示す図である。It is a figure which shows the relationship between time and stock quantity. 引取量が引取目標量から一定幅以上離れないという制約を説明するための図である。It is a figure for demonstrating the restriction | limiting that a taking-out amount does not leave | separate a fixed width or more from a taking-up target amount. 引取量が引取目標量から一定幅以上離れないという制約を説明するための図である。It is a figure for demonstrating the restriction | limiting that a taking-out amount does not leave | separate a fixed width or more from a taking-up target amount. マクロ最適化とミクロ最適化との関係を模式的に示した図である。It is the figure which showed typically the relationship between macro optimization and micro optimization. 積地における負荷の平準化を目的とする目的関数について説明するための図である。It is a figure for demonstrating the objective function aiming at the leveling of the load in a loading area. 揚地における負荷の平準化を目的とする目的関数について説明するための図である。It is a figure for demonstrating the objective function aiming at the leveling of the load in a landing. 配船計画の例を示す図である。It is a figure which shows the example of a ship allocation plan. 本発明の配合計画装置、配船計画装置として機能しうるコンピュータ装置のハードウェア構成例を示す図である。It is a figure which shows the hardware structural example of the computer apparatus which can function as a mixing | blending planning apparatus of this invention, and a ship allocation planning apparatus.
　以下、添付図面を参照して、本発明の好適な実施形態について説明する。本実施形態では、複数の製鉄所に、世界中に点在する山元（積地）から鉱石や石炭等の配合原材料を船舶による輸送で入荷し、それらを混合する例を説明する。すなわち、この例では、複数銘柄の配合原材料が、複数の供給元から船舶にて複数の供給先に輸送され、複数の供給先に入荷する。そして、この配合原材料が、各供給先において配合されて使用される。この際に、この複数銘柄の配合原材料の配合計画、及びこの複数銘柄の配合原材料は、積地である複数の供給元から揚地である複数の供給先に船舶によって輸送される。この船舶による輸送の際の配船計画を作成するのに好適な配合及び配船計画作成について、以下説明する。 Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings. In the present embodiment, an example will be described in which compound raw materials such as ore and coal are received by shipping by a ship from mountain bases (loading sites) scattered around the world at a plurality of steelworks, and are mixed. In other words, in this example, a plurality of branded raw materials are transported from a plurality of suppliers to a plurality of suppliers on a ship and arrive at a plurality of suppliers. And this compounding raw material is mix | blended and used in each supply destination. At this time, the blending plan of the plural brands of blended raw materials and the plural brands of blended raw materials are transported by a ship from a plurality of supply sources serving as loading areas to a plurality of supply destinations serving as landing sites. The composition suitable for creating the ship assignment plan for the transportation by the ship and the preparation of the ship assignment plan will be described below.
（システム構成）
　図１は、本発明の実施形態１から４に係る配合及び配船計画作成システムの概略構成を示す図である。なお、図１には例えば第２の配合計画作成装置３００が示されているが、実施形態１においては、これは使用されない。以下、複数の実施形態に概ね共通する構成、工程について、その概要を説明する。その後に、各実施形態に含まれる要素、工程について、詳細に記載する。
　１００は第１の配合計画作成装置であり、原材料の引取目標量に基づいて、複数銘柄の原材料を入荷して混合する配合計画を作成する。ここでは、製鉄所毎に原材料を配合する配合計画を作成する。これにより、各製鉄所において原材料を日々どれだけ使用していくかを示す、使用予定量が計画される。この第１の配合計画作成装置１００が、本発明でいう第１の配合計画作成部として機能するものである。 (System configuration)
FIG. 1 is a diagram showing a schematic configuration of a composition and ship allocation plan creation system according to Embodiments 1 to 4 of the present invention. In FIG. 1, for example, a second blending plan creation apparatus 300 is shown, but in the first embodiment, this is not used. Hereinafter, the outline of the configuration and steps that are generally common to the plurality of embodiments will be described. Thereafter, elements and processes included in each embodiment will be described in detail.
Reference numeral 100 denotes a first blending plan creation device, which creates a blending plan for receiving and mixing a plurality of brand raw materials based on a raw material take-up target amount. Here, a blending plan for blending raw materials for each steelworks is created. As a result, a planned usage amount is planned which indicates how much raw materials are used every day at each steelworks. This 1st mixing | blending plan preparation apparatus 100 functions as a 1st mixing | blending plan preparation part said by this invention.
　２００は配船計画作成装置であり、第１の配合計画作成装置１００により作成された配合計画に基づいて、複数銘柄の原材料（鉱石や石炭等）を複数の積地（世界中に点在する山元）から複数の揚地（製鉄所）に輸送する配船計画を作成する。本実施形態では、製鉄所毎の輸送費用平準化ではなく、全製鉄所合計での輸送費用をミニマム化する配船計画を作成することを目的としている。更には、輸送費用に加えて、原材料の購入費用を含めた費用をミニマム化する配船計画を作成することを目的としている。この配船計画作成装置２００が、本発明でいう配船計画作成部として機能するものである。 Reference numeral 200 denotes a ship allocation plan creation device. Based on the formulation plan created by the first formulation plan creation device 100, a plurality of brands of raw materials (ores, coals, etc.) are scattered in a plurality of places (in the world). Create a ship allocation plan for transportation from Yamamoto) to multiple landing sites (steel works). The purpose of this embodiment is to create a ship allocation plan that minimizes the transportation cost of all steelworks, not the leveling of transportation costs for each steelworks. Furthermore, the purpose is to create a ship allocation plan that minimizes the cost including the purchase cost of raw materials in addition to the transportation cost. This ship allocation plan creation apparatus 200 functions as a ship allocation plan creation part as referred to in the present invention.
　３００は第２の配合計画作成装置であり、配船計画作成装置２００により作成された配船計画に基づいて、複数銘柄の原材料を入荷して混合する配合計画を作成する。この第２の配合計画作成装置３００が、本発明でいう第２の配合計画作成部として機能するものである。 Numeral 300 is a second blending plan creation device that creates a blending plan for receiving and mixing raw materials of a plurality of brands based on the dispatching plan created by the dispatching plan creation device 200. This 2nd mixing | blending plan preparation apparatus 300 functions as a 2nd mixing | blending plan preparation part said by this invention.
　４００はデータベースであり、各装置１００～３００が計画を作成する上で使用するデータや、各装置１００～３００が作成した計画をコンピュータ５００が参照可能、更には修正可能な方式で格納する。これにより、各装置１００～３００が計画を作成する上で使用するデータや、各装置１００～３００が作成した計画の一括管理が可能となり、最新情報の共有化を図ることができる。 Reference numeral 400 denotes a database, which stores data used by each device 100 to 300 in creating a plan and plans created by each device 100 to 300 in a manner that can be referred to by the computer 500 and further corrected. As a result, it is possible to collectively manage the data used when each apparatus 100 to 300 creates a plan and the plans created by each apparatus 100 to 300, and the latest information can be shared.
　５００はコンピュータであり、データベース４００に格納されたデータを参照、更新したり、データベース４００にデータを格納したりする。なお、ここではコンピュータ５００を一つしか図示していないが、本実施形態では、複数台のコンピュータがＬＡＮやインターネットを介して接続される。コンピュータ５００には、例えばプロセスコンピュータ等と称される上位コンピュータや、各所（製鉄所、本社、船会社、山元等）に設置されたデータベース４００にアクセス可能なコンピュータ端末等がある。 Reference numeral 500 denotes a computer that refers to and updates data stored in the database 400 and stores data in the database 400. Although only one computer 500 is shown here, in the present embodiment, a plurality of computers are connected via a LAN or the Internet. The computer 500 includes, for example, a host computer called a process computer or the like, a computer terminal that can access the database 400 installed in each place (ironworks, head office, shipping company, Yamamoto, etc.).
　なお、図１に示すシステム構成は一例に過ぎず、それに限定されるものではない。例えば各装置１００～３００がそれぞれ一つの装置として構成されるかのように図示したが、装置１００～３００がそれぞれ複数の機器からなるコンピュータシステムで構成されるようにしてもかまわない。また、例えば第１の配合計画作成装置１００及び第２の配合計画作成装置３００が同様のアルゴリズムで配合計画を作成するような場合には、一つのコンピュータシステムが第１の配合計画作成装置１００及び第２の配合計画作成装置３００として機能するように構成してもよい。更には、本発明でいう第１の配合計画作成部、配船計画作成部、データベース部、第２の配合計画作成部が一つの装置として実現される場合も本発明の範疇にあるものとする。 Note that the system configuration shown in FIG. 1 is merely an example and is not limited thereto. For example, each of the devices 100 to 300 is illustrated as being configured as one device, but each of the devices 100 to 300 may be configured of a computer system including a plurality of devices. Further, for example, when the first blending plan creation device 100 and the second blending plan creation device 300 create a blending plan using the same algorithm, one computer system can be used for the first blending plan creation device 100 and You may comprise so that it may function as the 2nd mixing | blending plan preparation apparatus 300. FIG. Furthermore, the case where the first blending plan creation unit, the ship allocation plan creation unit, the database unit, and the second blending plan creation unit referred to in the present invention are realized as one device is also within the scope of the present invention. .
（第１の実施形態）
（第１の配合計画作成装置１００）
　第１の配合計画作成装置１００では、原材料の引取目標量に基づいて、複数銘柄の原材料を入荷して混合する配合計画を作成する。引取目標量とは、山元（積地）別、銘柄別の引取目標量（引取予定量）である。各山元とは、銘柄毎に、例えば年間といった期間中で、どれだけの量を引き取るかについて契約している。この期間中の引取量を、相当する期間に含まれる旬の数で割れば、旬毎の引取目標量が得られる。なお、本明細書において、旬は月を３つに分割した期間の単位を指す。 (First embodiment)
(First formulation planning device 100)
The first blending plan creation device 100 creates a blending plan for receiving and mixing a plurality of brands of raw materials based on the raw material take-up target amount. The collection target amount is a collection target amount (scheduled amount to be collected) by Yamamoto (loading place) and by brand. Each Yamamoto contracts with each brand to determine how much of it will be taken over, for example, during a year. By dividing the amount collected during this period by the number of seasons included in the corresponding period, the target amount for each season can be obtained. In addition, in this specification, season refers to the unit of the period which divided the month into three.
　図２は、第１の配合計画作成装置１００を含むシステム構成例を示す図である。図２に示すように、第１の配合計画作成装置１００は、配合計画を作成するに際して、操業者によるデータ設定を受け付けるか、或いはデータベース４００からデータを取り込む。この際、取り込まれるデータには、例えば以下のものが含まれる：配合計画を立案する上で必要となる計画作成期間、引取目標量、原材料の在庫状況、原材料の性状、原材料の単価を表す購入費用情報、船舶を利用する際の輸送費用情報を含む制約条件、及び前提条件。原材料の性状に関するデータは、例えば、石炭化度、各成分含有比率など、物理的、化学的な性状を含む。 FIG. 2 is a diagram illustrating a system configuration example including the first blending plan creation device 100. As shown in FIG. 2, the first blending plan creation device 100 accepts data setting by an operator or takes in data from a database 400 when creating a blending plan. In this case, the data to be captured includes, for example, the following: Purchase period that represents the planning period required for formulating the formulation plan, target volume, raw material stock status, raw material properties, raw material unit price Constraints and assumptions including cost information, shipping cost information when using a ship. The data on the properties of raw materials include physical and chemical properties such as the degree of coalification and the content ratio of each component.
　第１の配合計画作成装置１００は、多種類（複数銘柄）の原材料を入荷して混合する混合計画を、シミュレーションを実行して作成する。第１の配合計画作成装置１００は、原材料の需給バランス制約、混合後の性状制約を満たすように、配合計画として各銘柄の使用量（配合割合）、入荷量を求める。詳細は後述するが、第１の配合計画作成装置１００は、ＬＰ（線形計画法）、ＭＩＰ（混合整数計画法）、ＱＰ（２次計画法）等の数理計画法に則って構築された、原材料の需給バランス制約を表す数式モデル（「需給バランスモデル」とも称する）、及び、混合後の性状制約を表す数式モデル（「性状モデル」とも称する）を設定することにより配合計画の最適化を図る。
　数式モデルの設定とは、数式モデルの展開とも呼ばれる一連の工程であり、以下に説明するような工程を含み、本実施形態の装置の各部、あるいは方法によって行われる。本実施形態において、数式モデルは、船舶数や港数などの各条件の変化に対応できるように、抽象的な形式で予め構築・定式化されている。この数式モデルに対して、各配列の添え字の最大数（例えば船舶数を表す）や、式中の係数及び定数の値などを、計画立案の条件に沿って具体的に定める。 The first blending plan creating apparatus 100 creates a mixing plan for receiving and mixing various types (a plurality of brands) of raw materials by executing a simulation. The 1st mixing | blending plan preparation apparatus 100 calculates | requires the usage-amount (mixing ratio) and arrival amount of each brand as a mixing | blending plan so that the supply-and-demand balance restrictions of a raw material and the property restrictions after mixing may be satisfy | filled. Although details will be described later, the first blending plan creation device 100 is constructed according to mathematical programming methods such as LP (linear programming), MIP (mixed integer programming), QP (quadratic programming), Optimize the formulation plan by setting a mathematical model that represents the supply-demand balance constraint for raw materials (also referred to as “supply-demand balance model”) and a mathematical model that represents the property constraint after mixing (also referred to as “property model”) .
The setting of the mathematical model is a series of steps also called development of the mathematical model, and includes the steps described below, and is performed by each unit or method of the apparatus according to the present embodiment. In the present embodiment, the mathematical model is constructed and formulated in advance in an abstract format so as to cope with changes in conditions such as the number of ships and the number of ports. For this mathematical model, the maximum number of subscripts in each array (for example, representing the number of ships), the values of coefficients and constants in the formula, etc. are specifically determined according to the planning conditions.
　表示部３０３では、第１の配合計画作成装置１００で求められた各銘柄の使用量（配合割合）、入荷量、在庫推移グラフ、各種帳票を表示する。 In the display unit 303, the usage amount (mixing ratio) of each brand, the amount received, the inventory transition graph, and various forms obtained by the first blending plan creation device 100 are displayed.
　操業者評価部３０４では、求められた配合計画を様々な観点（例えば、在庫推移、性状等）から操業者が評価し、満足のいく結果でなければ、必要に応じて配合割合等を修正する。その際に、必要に応じて目的関数の重みや評価の指標を変えたり、数式モデルを設定する対象期間・計画確定期間を変えたりする。また、全部の或いは指定した処理のみ使用量の固定をする等、操業者の意志を反映させた数式モデルの設定が可能である。そして、第１の配合計画作成装置１００で再度配合計画を作成し直す。 In the operator evaluation unit 304, the operator evaluates the obtained blending plan from various viewpoints (for example, inventory transition, properties, etc.), and corrects the blending ratio and the like as necessary if the result is not satisfactory. . At that time, the weight of the objective function and the evaluation index are changed as necessary, and the target period / plan decision period for setting the mathematical model is changed. In addition, it is possible to set a mathematical model reflecting the operator's will, such as fixing the amount of use only for all or specified processes. Then, the blending plan is created again by the first blending plan creation device 100.
　図３は、第１の配合計画作成装置の基本的な構成を示すブロック図である。図３に示すように、第１の配合計画作成装置１００は、シミュレータ（在庫推移シミュレータ３１１、性状シミュレータ３１２を含む）、本発明でいう数式モデル設定部として機能するモデル設定部（需給バランスモデル設定部３１３、性状モデル設定部３１４を含む）、本発明でいう最適化計算部として機能する計画部３１５を含んで構成され、更に入出力部を併せ持つ。 FIG. 3 is a block diagram showing the basic configuration of the first formulation planning device. As shown in FIG. 3, the first blend plan creation device 100 includes a simulator (including an inventory transition simulator 311 and a property simulator 312), a model setting unit (demand / supply balance model setting) that functions as a mathematical model setting unit in the present invention. 313, a property model setting unit 314), and a planning unit 315 functioning as an optimization calculation unit in the present invention, and further includes an input / output unit.
　在庫推移シミュレータ３１１は、各原材料の需給状態（在庫推移）を計算するシミュレータである。性状シミュレータ３１２は、原材料を混合した後の性状を計算するシミュレータである。在庫推移シミュレータ３１１、性状シミュレータ３１２が互いに連動することで、原材料の在庫推移、混合後の性状を計算する。 The inventory transition simulator 311 is a simulator for calculating the supply and demand state (inventory transition) of each raw material. The property simulator 312 is a simulator that calculates properties after mixing raw materials. The inventory transition simulator 311 and the property simulator 312 work together to calculate the inventory transition of raw materials and the properties after mixing.
　本実施形態においては、例えば以下の情報を含む、入力データ３１６に基づいて数式モデルの設定処理を行う：配合計画を立案する上で必要となる計画作成期間、引取目標量、原材料の在庫状況、原材料の性状、原材料の単価を表す購入費用情報、及び船舶を利用する際の輸送費用情報等。配合計画の立案開始日時から予め設定された最適化期間分を対象として、予め設定した時間精度に基づいて、ＬＰ（線形計画法）、ＭＩＰ（混合整数計画法）、ＱＰ（２次計画法）等の数理整数計画法等に則り、需給バランスモデル設定部３１３にて需給バランス制約（在庫制約）を表す数式モデルが設定され、性状モデル設定部３１４により性状制約を表す数式モデルが設定される。 In the present embodiment, for example, the mathematical model setting process is performed based on the input data 316 including the following information: a plan creation period, a take-up target amount, a raw material inventory status, which are necessary for formulating a formulation plan, Properties of raw materials, purchase cost information indicating the unit price of raw materials, and transportation cost information when using a ship. LP (Linear Programming), MIP (Mixed Integer Programming), QP (Secondary Programming) based on the preset time accuracy for the optimization period set in advance from the formulation planning start date and time In accordance with mathematical integer programming such as, a mathematical model representing supply / demand balance constraints (inventory constraints) is set by the supply / demand balance model setting unit 313, and a mathematical model representing property constraints is set by the property model setting unit 314.
　需給バランスモデル設定部３１３、性状モデル設定部３１４により設定された数式モデルを用いて、在庫を切らさないようにするとともに要求される性状を満足し、かつ、費用（原材料の購入費用及び輸送費用）をミニマム化して配合計画を作成するように、計画部３１５が最適化計算を行う。この最適化計算の結果に基づいて、在庫推移シミュレータ３１１、性状シミュレータ３１２に対する計算指示を作成する。この計算指示を受けて、在庫推移シミュレータ３１１が、在庫推移をシミュレートし、性状シミュレータ３１２が計画に従って製造される製品・半製品の性状をシミュレートする。例えば、鉄鋼業においては、石炭を混合して焼き固めたコークス（製品）、鉄鉱石を還元して得られる銑鉄を精錬した溶鋼を凝固させたスラブ（半製品）等の性状をシミュレートする。 Using mathematical formula models set by the supply and demand balance model setting unit 313 and the property model setting unit 314, the inventory is not cut and the required properties are satisfied, and the cost (raw material purchase cost and transportation cost) The planning unit 315 performs optimization calculation so as to create a blending plan by minimizing. Calculation instructions for the inventory transition simulator 311 and the property simulator 312 are created based on the result of the optimization calculation. In response to this calculation instruction, the inventory transition simulator 311 simulates the inventory transition, and the property simulator 312 simulates the properties of the products and semi-finished products manufactured according to the plan. For example, in the iron and steel industry, properties such as coke (product) that is baked and hardened by mixing coal, and slab (semi-finished product) that is obtained by solidifying molten steel obtained by refining pig iron obtained by reducing iron ore are simulated.
　かかる第１の配合計画作成装置１００によれば、従来のように予め決められたルールに基づいて計算指示が行われるのではなく、計画部３１５により行われた最適化計算の結果に基づいた計算指示を在庫推移シミュレータ３１１、性状シミュレータ３１２に出力する。このため、そのときの事象に応じた最適な計算指示を確実に行うことが可能となる。 According to the first blending plan creation apparatus 100, calculation is not performed based on a predetermined rule as in the prior art, but calculation based on the result of optimization calculation performed by the planning unit 315. The instruction is output to the inventory transition simulator 311 and the property simulator 312. For this reason, it is possible to reliably perform an optimal calculation instruction according to the event at that time.
　例えば、図７に示すように予め設定された計画確定期間分について、在庫推移シミュレータ３１１、性状シミュレータ３１２によるシミュレーションが、行われる。このシミュレーションが終了すると、立案開始日が更新され、更新前の計画確定期間の最終状態、つまり更新後の立案開始日での在庫推移、性状の情報に基づいて、新たな最適化期間分の需給バランスモデル設定部３１３により在庫制約を表す数式モデルが設定され、性状モデル設定部３１４により性状制約を表す数式モデルが設定される。設定後の数式モデルは、計画部３１５に与えられる。このように、計画部３１５に、在庫推移、性状の情報に基づいて設定された数式モデルが与えられると、これを用いて、最適化計算が実行される。 For example, as shown in FIG. 7, a simulation by the inventory transition simulator 311 and the property simulator 312 is performed for a predetermined plan fixed period. When this simulation is completed, the planning start date is updated, and the supply and demand for the new optimization period are updated based on the final state of the plan finalization period before the update, that is, inventory transition and property information on the planning start date after the update. The balance model setting unit 313 sets a mathematical model representing inventory constraints, and the property model setting unit 314 sets a mathematical model representing property constraints. The mathematical model after the setting is given to the planning unit 315. Thus, when the planning unit 315 is given a mathematical model set based on inventory transition and property information, optimization calculation is executed using the mathematical model.
　以上のように、各シミュレータ（在庫推移シミュレータ３１１、性状シミュレータ３１２）と、各モデル設定部（需給バランスモデル設定部３１３、性状モデル設定部３１４）と、計画部３１５とを連動させた詳細なシミュレーションを実行することで、最適な配合計画を作成することができる。すなわち、本実施形態において行われるシミュレーションは、従来のような所定のルールに基づくシミュレーションではなく、最適化計算の結果に基づいて行われる。このため、１回のシミュレーションを実行するだけでも、理論的な最適解を確実に得ることが可能となる。この構成により、従来のようにシミュレーション結果を評価してシミュレーションを何回も繰り返して実行する必要がなく、シミュレーション結果３１７を迅速に、かつ、高精度に作成することができる。したがって、配合計画を作成する対象が大規模であっても実用時間内に作成することが十分に可能である。 As described above, detailed simulations in which each simulator (the inventory transition simulator 311 and the property simulator 312), each model setting unit (the supply and demand balance model setting unit 313, the property model setting unit 314), and the planning unit 315 are linked. By executing this, it is possible to create an optimal blending plan. That is, the simulation performed in the present embodiment is performed based on the result of the optimization calculation, not the simulation based on the predetermined rule as in the prior art. For this reason, it is possible to surely obtain a theoretically optimal solution by executing only one simulation. With this configuration, it is not necessary to evaluate the simulation result and repeat the simulation many times as in the prior art, and the simulation result 317 can be created quickly and with high accuracy. Therefore, even if the target for creating the formulation plan is large, it can be sufficiently created within the practical time.
　また、従来法では、計画作成期間が長くなると、計算対象となる期間が長くなり、これにつれて、問題規模が急激に大きくなるため、求解が不可能になる問題があった。しかし、本手法では全計画作成期間を、これより短い最適化期間に分割することで、問題規模を小さくすることができる。このため、全体としての計画作成期間が長くなっても、問題を解くことが可能となる。上述のようにして得られたシミュレーション結果３１７を配合計画として出力する。 Also, in the conventional method, when the plan creation period becomes long, the period to be calculated becomes long, and as the problem scale increases rapidly with this, there is a problem that the solution is impossible. However, in this method, the problem scale can be reduced by dividing the entire plan creation period into shorter optimization periods. For this reason, even if the plan preparation period as a whole becomes long, it becomes possible to solve a problem. The simulation result 317 obtained as described above is output as a blending plan.
　また、需給バランスモデル設定部３１３、性状モデル設定部３１４により設定するモデルの規模が非常に大きい場合や、制約条件が非常に多くて複雑な場合でも、在庫推移シミュレータ３１１、性状シミュレータ３１２に記載された需給バランス制約、性状制約のうち、配合計画作成に影響が大きく、重要な部分のみを抽出し、この抽出された部分のみを、需給バランスモデル設定部３１３、性状モデル設定部３１４に取り込んでもよい。これによって、需給バランスモデル設定部３１３、性状モデル設定部３１４の数式モデルの規模を適切な範囲にして、実用的な時間内で最適化計算を行うことができる。在庫推移シミュレータ３１１、性状シミュレータ３１２は、考慮すべき需給バランス制約、性状制約を全て記載することができるので、１回のシミュレーションを実行して作成された配合計画は現実に実行可能となることが保証される。 Even when the scale of the model set by the supply / demand balance model setting unit 313 and the property model setting unit 314 is very large, or when the constraint condition is very large and complicated, it is described in the inventory transition simulator 311 and the property simulator 312. Of the supply / demand balance constraints and property constraints, it is possible to extract only the important parts that have a great influence on the formulation plan creation, and import only the extracted parts into the supply / demand balance model setting unit 313 and the property model setting unit 314. . Thereby, the optimization calculation can be performed within a practical time by setting the scale of the mathematical model of the supply and demand balance model setting unit 313 and the property model setting unit 314 to an appropriate range. Since the inventory transition simulator 311 and the property simulator 312 can describe all supply and demand balance constraints and property constraints to be considered, the blending plan created by executing one simulation may be actually executable. Guaranteed.
　上述したように、本実施形態においては、シミュレータ（在庫推移シミュレータ３１１、性状シミュレータ３１２）とモデル設定部（需給バランスモデル設定部３１３、性状モデル設定部３１４）と計画部３１５とを連動させて配合計画を作成する。このため、以下の効果が得られる：（１）シミュレーションを繰り返して実行せずに配合計画を作成することができる；（２）配合計画作成に影響が大きい重要な部分の制約のみを計画部３１５に取り込むことで、計算時間を短縮することができる；（３）大規模な問題を解くことが可能になる。 As described above, in the present embodiment, the simulator (the inventory transition simulator 311 and the property simulator 312), the model setting unit (the supply and demand balance model setting unit 313, the property model setting unit 314), and the planning unit 315 are combined. Create a plan. For this reason, the following effects can be obtained: (1) A blending plan can be created without repeatedly executing a simulation; (2) Only a critical part of a constraint that has a large influence on the blending plan creation is planned 315. The calculation time can be shortened by incorporating into (3); (3) Large-scale problems can be solved.
　以下、第１の配合計画作成装置１００の構成及びこの装置を用いて実行する配合計画作成方法の各ステップをより詳細に説明する。図４は、図３を用いて説明した第１の配合計画作成装置１００の基本的な構成に対する、配合計画作成装置の詳細な構成を示す図である。また、図５は、この装置を用いて実行する配合計画作成方法の各ステップを示すフローチャートである。 Hereinafter, the configuration of the first blending plan creation device 100 and each step of the blending plan creation method executed using this device will be described in more detail. FIG. 4 is a diagram illustrating a detailed configuration of the blending plan creation apparatus with respect to the basic configuration of the first blending plan creation apparatus 100 described with reference to FIG. 3. FIG. 5 is a flowchart showing each step of the formulation plan creation method executed using this apparatus.
　配合計画作成の概要として、例えば図６に示すように、以下のような計算、調整の工程が含まれる：複数ある製鉄所（揚港）ａ～ｃでの原材料（銘柄）の需給バランスを取ること；（各銘柄Ａ～Ｎの在庫を切らさない等）要求される性状を満足させること；かつ、費用（原材料の購入費用及び輸送費用）をミニマム化すること；前記の条件を満たすような配合計画として、製鉄所ａ～ｃ毎の各銘柄Ａ～Ｎの使用量（配合割合）、入荷量を決定すること。ここで、製鉄所毎の使用量の合計量である予定使用量は、入力データとして与えられる。このため、配合割合（％）＝使用量／予定使用量×１００となる。このため、使用量、配合割合の一方が決定されれば、他方が決定されることとなる。 For example, as shown in Fig. 6, the outline of the formulation plan includes the following calculation and adjustment processes: Balance supply and demand of raw materials (brands) at multiple steelworks (lift ports) a to c Satisfying the required properties (such as not stocking out the stocks A to N); and minimizing costs (raw material purchase and transportation costs); As a plan, determine the usage (mixing ratio) and receipt of each brand A to N for each steelworks a to c. Here, the planned usage amount, which is the total usage amount for each steelworks, is given as input data. Therefore, the blending ratio (%) = used amount / planned used amount × 100. For this reason, if one of usage-amount and a mixture ratio is determined, the other will be determined.
（１）入力データの取り込み（図４の入力データ取込み部３５１、図５のステップＳ３０１）
　本処理に必要な情報（引取目標量、原材料の在庫状況、原材料の性状、原材料の単価を表す購入費用情報、船舶を利用する際の輸送費用情報等）をオンラインにて読み込み、必要に応じて操業者が修正を加える。 (1) Acquisition of input data (input data acquisition unit 351 in FIG. 4, step S301 in FIG. 5)
Information necessary for this processing (collection target amount, raw material inventory status, raw material properties, purchase cost information indicating the unit price of raw materials, transportation cost information when using a ship, etc.) is read online and as required. The operator makes corrections.
　ここで、入力データ取込み部３５１により取り込まれる引取目標量は、山元（積地）別、銘柄別の引取目標量（引取予定量）を表す情報である。各山元とは原材料の銘柄毎に、例えば年間等の期間において、どれだけの量の原材料を引き取るか（引取量）について契約している。この期間全体の引取量を月数で割れば、月毎の引取目標量が得られる。この引取目標量に近づけるように、入荷することが求められる。しかし、年間で数万トン程度の引取量の上下へのぶれは山元との交渉により、許容範囲内となる。また、契約によっては、所定の銘柄については所定の期間は引取しないといった契約も考えられる。そういった例外的な条件を、計算に含めてもよい。例えば、原材料Ａのある月での引取目標量が５万トンの場合で、引取目標量からの上下へのぶれが年間で６万トン（月当たり５千トン）の場合を考える。この場合、当該月の入荷予定について、上限（入荷予定量上限）５万トン＋５千トン、下限（入荷予定量下限）５万トン－５千トンの間の範囲が、許容範囲となる。 Here, the take-up target amount taken in by the input data take-in unit 351 is information indicating the take-up target amount (scheduled take-up amount) for each Yamamoto (loading place) and each brand. Each Yamamoto contracts with each brand of raw materials to determine how much raw material is to be collected (collected amount) in a period of, for example, a year. By dividing the total amount collected during this period by the number of months, a monthly target amount can be obtained. It is required to arrive so as to be close to the take-up target amount. However, up and down fluctuations of about tens of thousands of tons per year will be within the allowable range through negotiations with Yamamoto. In addition, depending on the contract, there may be a contract in which a predetermined brand is not picked up for a predetermined period. Such exceptional conditions may be included in the calculation. For example, let us consider a case where the target amount of raw material A in a certain month is 50,000 tons, and the up / down fluctuation from the target amount is 60,000 tons per year (5,000 tons per month). In this case, the upper limit (the upper limit of the expected arrival quantity) 50,000 tons + 5,000 tons and the lower limit (the lower limit of the expected arrival quantity) 50,000 tons to 5,000 tons are acceptable.
　原材料の在庫状況は、計画作成期間の初日における製鉄所別、銘柄別の在庫量（トン数）を表す情報である。 The stock status of raw materials is information indicating the stock quantity (ton tonnage) by steelworks and brand on the first day of the planning period.
　原材料の性状は、原材料毎の成分等の性状を表す情報である。例えば、原材料である鉄鉱石の性状としては、Fe2O3、Fe3O4、SiO2、Al2O3等の原材料についての、性状情報が含まれる。 The property of the raw material is information indicating the property of the component for each raw material. For example, the property of iron ore that is a raw material includes property information on raw materials such as Fe 2 O 3 , Fe 3 O 4 , SiO 2 , and Al 2 O 3 .
　原材料の購入費用情報は、山元（積地）別、銘柄別の原材料の単価（＄／トン（ｔｏｎ、ｔ））を表す情報である。 The raw material purchase cost information is information indicating the unit price ($ / ton (ton, t)) of the raw material by Yamamoto (loading place) and by brand.
　船舶を利用する際の輸送費用情報は、図１５、１６に示すように、船舶リストにリストアップされている船舶を利用する場合のフレート、及び、船舶リストにリストアップされている船舶を利用する場合の積揚港別の滞船料、を表す情報である。また、船舶を利用する際の輸送費用情報には、銘柄別・揚港（揚地）別見做しフレートを表す情報も含まれる。輸送費用は、本来上述した船舶別・積港別・揚港別フレートにより、一意的に定まるものである。しかし、第１の配合計画作成装置１００の作業段階においては、入荷に関して、原材料を積載する船舶が決定されていない。これら積載する船舶が未決定の原材料に対して、原材料の輸送費用を見積もるために、銘柄別・揚港別のフレートが必要となる。ここで、銘柄別・揚港別フレートは、原材料を積載する船舶の選択等によって本来はフレートが異なるため、一意に決定することができない。そこで、銘柄別・揚港別フレートに替え、銘柄別・揚港別に、概算である見做しフレートの情報を取得する。銘柄別・揚港別見做しフレートとしては、例えば経験等に基づいて設定した銘柄別・揚港別フレート、或いは過去実績から統計的手法、例えば銘柄別・揚港別に過去実績のフレートを収集し、その平均値を銘柄別・揚港別フレートとして見做した銘柄別・揚港別見做しフレートが予めリストアップされている。 As shown in FIGS. 15 and 16, the transportation cost information when using a ship uses a freight when using a ship listed in the ship list and a ship listed in the ship list. This is information indicating the berthing fee for each port of loading. In addition, the transportation cost information when using a ship includes information indicating the freight rate by brand and landing port. The transportation cost is uniquely determined by the freight rate by ship, by port, and by port. However, in the operation phase of the first blending plan creation device 100, the ship on which the raw material is loaded is not determined for the arrival. In order to estimate the transportation cost of raw materials for those raw materials that have not yet been decided by the ship to be loaded, it is necessary to have a freight by brand / shipping port. Here, the freight by brand / shipping port cannot be uniquely determined because the fret is originally different depending on the selection of a ship carrying raw materials. Therefore, instead of the freight by brand / shipping port, information on the estimated freight is obtained for each brand / shipping port. As for the freight rate for each issue / shipping port, for example, the freight rate for each issue / shipping port set based on experience, etc. However, the freight rates classified by brand / shipping port, where the average value is regarded as the freight rate by brand / shipping port, are listed in advance.
　以上説明した入力データ取込み部３５１及びステップＳ３０１が、本発明でいう第１の配合計画作成部のデータ取込み部及びそれによる処理の例である。 The input data fetching unit 351 and step S301 described above are examples of the data fetching unit of the first blending plan creating unit referred to in the present invention and processing by it.
（２）配合計画作成期間の設定（図４の計画作成期間設定部３５２、図５のステップＳ３０２）
　配合計画を作成する期間を設定する。この作成期間は立案者の必要に応じて任意の期間を設定可能とする。ここでは、一例として１０日間分を立案する。 (2) Setting of formulation plan creation period (plan creation period setting unit 352 in FIG. 4, step S302 in FIG. 5)
Set the period for creating a recipe. This creation period can be set as desired according to the planner's needs. Here, 10 days is planned as an example.
（３）配合計画作成時間精度の設定（図４の時間精度設定部３５３、図５のステップＳ３０３）
　配合計画を作成する時間精度並びにシミュレーション精度を設定する。この時間精度並びにシミュレーション精度は、立案者の必要に応じて個別に任意の精度を設定可能とする。例えば立案の細かな精度を必要とする計画作成期間の前半では精度を細かくし、粗い計画で十分な計画作成期間の後半では精度を粗くすることで、十分な精度と短い計算時間での、効率的な計画作成が可能になる。 (3) Setting of mixing plan creation time accuracy (time accuracy setting unit 353 in FIG. 4, step S303 in FIG. 5)
Set the time accuracy and simulation accuracy to create a recipe. The time accuracy and the simulation accuracy can be arbitrarily set individually according to the planner's needs. For example, by making the accuracy fine in the first half of the planning period that requires fine planning accuracy, and by making the precision coarse in the second half of the planning period that is sufficient for rough planning, efficiency with sufficient accuracy and short calculation time Planning becomes possible.
（４）最適化期間の設定（図４の最適化期間設定部３５４、図５のステップＳ３０４）
　配合計画を作成する最適化期間を設定する。この最適化期間は立案者の必要に応じて個別に任意の対象期間を設定可能とする。ここでは、一例として、計画作成期間を通して、最適化期間を３日間とする。 (4) Optimization period setting (optimization period setting unit 354 in FIG. 4, step S304 in FIG. 5)
Set the optimization period for creating a recipe. This optimization period can be set to any target period individually as required by the planner. Here, as an example, the optimization period is 3 days throughout the plan creation period.
（５）計画確定期間の設定（図４の計画確定期間設定部３５５、図５のステップＳ３０５）
　配合計画を確定する計画確定期間を設定する。この計画確定期間は、立案者の必要に応じて個別に任意の期間を設定可能とする。例えば、立案の細かな精度を必要とする計画作成期間の前半では計画確定期間を短くし、粗い計画で十分な計画作成期間の後半では計画確定期間を長くする。このことで、十分な精度を持ちながら短い計算時間で、効率的な計画作成が可能になる。ここでは、一例として、計画確定期間を１日に設定する。この場合は、数式モデルに対する解に基づいてシミュレートした結果得られる配合計画に対しては、計画作成期間を通して最初の１日分を確定する。 (5) Setting of plan fixed period (plan fixed period setting unit 355 in FIG. 4, step S305 in FIG. 5)
Set the plan confirmation period to finalize the recipe. This plan decision period can be set to an arbitrary period individually as required by the planner. For example, the plan finalization period is shortened in the first half of the plan creation period that requires fine planning accuracy, and the plan finalization period is lengthened in the second half of the plan preparation period sufficient for a rough plan. This makes it possible to create an efficient plan in a short calculation time with sufficient accuracy. Here, as an example, the plan confirmation period is set to one day. In this case, for the blending plan obtained as a result of simulation based on the solution for the mathematical model, the first day is determined throughout the plan creation period.
（６）配合計画の需給バランス制約を数式モデルに設定（図３の基本的構成図の需給バランスモデル設定部３１３、図４の需給バランスモデル設定部３５６、図５のステップＳ３０６）
　入力データ取込み部３５１により取り込まれたデータの全部又は一部に基づいて、設定された最適化期間分を、設定された時間精度で、需給バランス制約を数式モデルに対して設定する。 (6) Supply / demand balance constraints of the composition plan are set in the mathematical model (supply / demand balance model setting unit 313 in the basic configuration diagram in FIG. 3, supply / demand balance model setting unit 356 in FIG. 4, step S306 in FIG. 5).
Based on all or part of the data fetched by the input data fetching unit 351, the supply / demand balance constraint is set for the mathematical model with the set optimization period for the set optimization period.
　各銘柄の使用量を表す変数を下記の（式１）に示すように定義する。また、銘柄の在庫量を表す変数を下記の（式２）に示すように定義する。また、各銘柄の入荷量を表す変数を下記の（式３）に示すように定義する。なお、式中の「所」は、本実施形態では揚地と対応する製鉄所を示す。 ∙ Define the variables that represent the amount of each brand used as shown in (Equation 1) below. Further, a variable representing the stock amount of the brand is defined as shown in the following (Formula 2). Moreover, the variable showing the arrival amount of each brand is defined as shown in the following (Formula 3). In addition, "place" in a type | formula shows the steelworks corresponding to a landing site in this embodiment.
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000002
Figure JPOXMLDOC01-appb-M000002
Figure JPOXMLDOC01-appb-M000003
Figure JPOXMLDOC01-appb-M000003
　需給情報を基に設定した数式モデル、つまり需給バランス制約モデルを以下に示す。ここでは、各銘柄の在庫量が、一定の安全在庫量と呼ばれる値以上であることが要求される。この場合の制約は、下記の（式４）と表される。 The following is a mathematical model set based on supply and demand information, that is, a supply and demand balance constraint model. Here, the stock amount of each brand is required to be equal to or greater than a value called a certain safety stock amount. The constraint in this case is expressed as (Equation 4) below.
Figure JPOXMLDOC01-appb-M000004
Figure JPOXMLDOC01-appb-M000004
　また、各銘柄の在庫量は、前日の在庫量、前日の入荷量、前日の使用量より決定される。この場合の関係を表す制約式は、下記の（式５）と表される。つまり、当日の在庫量は、前日の在庫量と当日に入荷（荷揚）する量を足した値から、当日の使用量を引いた値となる。 In addition, the stock quantity of each brand is determined from the inventory quantity of the previous day, the arrival quantity of the previous day, and the usage quantity of the previous day. The constraint equation representing the relationship in this case is expressed as the following (Equation 5). In other words, the stock quantity on the current day is a value obtained by subtracting the use quantity on the current day from the value obtained by adding the stock quantity on the previous day and the quantity received (unloaded) on the current day.
Figure JPOXMLDOC01-appb-M000005
Figure JPOXMLDOC01-appb-M000005
　また、各銘柄の使用量のある日の合計は、当該日の全銘柄合計に対して予定された使用量と一致する必要がある。この場合の関係を表す制約式は、下記の（式６）と表される。 Also, the total amount of usage for each brand on a certain day must match the planned usage for all brands on that day. The constraint equation representing the relationship in this case is expressed as (Equation 6) below.
Figure JPOXMLDOC01-appb-M000006
Figure JPOXMLDOC01-appb-M000006
　また、各種原材料の購買に対する要因等に鑑み、操業者は、目標とする配合割合を設定し、この与えた目標とする配合割合に近い配合割合を実現する配合計画が作成されることを求める。つまり配合割合が操業者の想定と大きくかけ離れると、想定した購買量を満たせなくなったり、購買量を越えたり、また操業設備に無理な操業を及ぼすことが想定される。このため、目標として与えた配合割合に近い配合割合が出力されることが必要となる。上記機能を実現するための制約を以下に示す。つまり、銘柄の使用量から使用目標量（目標とする配合割合）（定数）を引いた値を、使用目標量からの溢れ量の変数として定義する。ここで、使用量と使用目標量が近い量を取る程良い計画であるため、この溢れ量は少ない程良い。上記理由のため、後述するように、この溢れ量が、目的関数の項目として追加され、最適化によってミニマム化される。同様に銘柄の使用目標量から使用量を引いた値を、使用目標量からの不足量の変数として定義する。ここで、使用量と使用目標量は近い量を取る程良い計画であるため、この不足量は少ない程良い。上記理由のため、後述するようにこの不足は、目的関数の項目として追加され、最適化によってミニマム化される。この場合、各銘柄の使用量、使用目標量、溢れ量、不足量との関係を表す制約式は下記の（式７）と表される。つまり、溢れが生じる場合は使用量から溢れ量を引き、不足が生じる場合は不足量を足すと、使用目標量と一致する。 Further, in view of factors for purchasing various raw materials, the operator sets a target blending ratio and requests that a blending plan that realizes a blending ratio close to the given blending ratio is created. In other words, if the blending ratio is far from the operator's assumption, it is assumed that the assumed purchase amount cannot be satisfied, the purchase amount is exceeded, or the operation facility is unreasonably operated. For this reason, it is necessary to output a blending ratio close to the blending ratio given as a target. The restrictions for realizing the above functions are shown below. That is, a value obtained by subtracting the target usage amount (target mixture ratio) (constant) from the brand usage amount is defined as a variable of the overflow amount from the usage target amount. Here, since the plan is such that the usage amount and the usage target amount are close, the smaller the overflow amount, the better. For the above reason, as described later, this overflow amount is added as an item of the objective function, and is minimized by optimization. Similarly, a value obtained by subtracting the use amount from the use target amount of the brand is defined as a variable of the shortage amount from the use target amount. Here, since the plan is such that the usage amount and the usage target amount are close to each other, the smaller the shortage amount, the better. For the above reason, as described later, this shortage is added as an item of the objective function and is minimized by optimization. In this case, the constraint equation representing the relationship between the usage amount, the usage target amount, the overflow amount, and the shortage amount of each brand is expressed as the following (Equation 7). That is, if overflow occurs, the overflow amount is subtracted from the usage amount, and if there is a shortage, the shortage amount is added to match the target usage amount.
Figure JPOXMLDOC01-appb-M000007
Figure JPOXMLDOC01-appb-M000007
　更に、前日の配合割合とその翌日の配合割合が大きく乖離すると、操業に困難を来たす。つまり、別原材料を使用するための段取り時間の増加や、設備の故障の原因となる。このため、前日の配合割合とその翌日の配合割合が大きく乖離することがない配合計画が求められる。上記機能を実現するため、銘柄の当該日の使用量と前日の使用量との差について、上限量を表す変数を下記の（式８）に示すように定義する。 Furthermore, if the blending ratio of the previous day and the blending ratio of the next day are greatly different, operation will be difficult. That is, it causes an increase in setup time for using another raw material and a failure of equipment. For this reason, the mixing | blending plan in which the mixing | blending ratio of the previous day and the mixing ratio of the following day do not largely diverge is calculated | required. In order to realize the above function, a variable representing the upper limit amount is defined as shown in (Equation 8) below for the difference between the usage amount of the brand on the day and the usage amount on the previous day.
Figure JPOXMLDOC01-appb-M000008
Figure JPOXMLDOC01-appb-M000008
　この変数を用いて上記を実現するための制約を下記の（式９）に示す。つまり、銘柄の当該日の使用量から当該日前日の使用を引いた値は、当該日の使用量と当該日前日の使用量との差以下とする。ここで、当該日の使用量と当該日前日の使用量とが近い量を取る程、好適な計画である。上記理由のため、後述するように、この溢れ量は、目的関数の項目として追加され、最適化によってミニマム化される。同様に、銘柄の当該日前日の使用量から当該日の使用を引いた値に関しても、制約式として定式化する。 Constraints for realizing the above using this variable are shown in (Equation 9) below. That is, the value obtained by subtracting the use of the day before the day of use from the day of the brand is set to be equal to or less than the difference between the use of the day and the use of the day before. Here, the closer the usage amount of the day and the usage amount of the day before, the better the plan. For the above reason, as described later, this overflow amount is added as an item of the objective function, and is minimized by optimization. Similarly, a value obtained by subtracting the use of the day from the use amount of the brand on the day before is formulated as a constraint expression.
Figure JPOXMLDOC01-appb-M000009
Figure JPOXMLDOC01-appb-M000009
　また、各銘柄の入荷量は、入荷予定量として与えられた量の範囲内に入っていることが要求される。この場合の関係を表す制約式は、下記の（式１０）、（式１１）と表される。つまり、当該月に入荷する入荷量の合計は当該月の入荷予定量上限以下、入荷予定量下限以上になる必要がある。 In addition, it is required that the arrival amount of each brand is within the range given as the expected arrival amount. The constraint equations representing the relationship in this case are expressed by the following (Equation 10) and (Equation 11). In other words, the total amount of arrival in the month needs to be less than or equal to the expected arrival amount upper limit and more than the expected arrival amount lower limit for the month.
Figure JPOXMLDOC01-appb-M000010
Figure JPOXMLDOC01-appb-M000010
　また、第１の配合計画作成装置１００では、引取目標量に基づいて、配合計画を作成する。後述するように、最適化（ステップＳ１０３～Ｓ１０６）及びシミュレーション（Ｓ１０７）を含む一連の工程は、複数ループ反復して実行できる。初回ループでは、ステップＳ３０１で取り込んだデータに基づいて、後述する数式モデルの設定を行い、次回ループ以降では、シミュレータでのシミュレーション結果を反映させて、数式モデルの設定を行う。引取目標量制約に関しての数式モデル設定では、例えば以下の条件を考慮する：最適化する引取量（入荷量）が引取目標量から一定幅以上離れないこと；及び、引取の可否（前述したように所定の銘柄については所定の期間は引取しないといった事情もありうる）。ここで、引取量が引取目標量から一定幅以上離れないという制約を定式化する方法として、例えば図８Ａに示すように、単に旬毎（或いは月毎）の引取目標量に対して、上下の許容範囲を設定し、入荷量がその許容範囲に含まれることを制約条件とする方法、または構成が考えられる。しかし、その場合、例えば入荷量が許容範囲の下限を満たしているが旬毎の引取目標量を下回る状況が続いたような場合、年間の累積入荷量は、年間の引取目標量を下回ることもありうる。そこで、図８Ｂに示すように、旬毎（或いは月毎）に立案開始日から該当する旬までの引取目標量累積及び引取量累積を算出し、引取目標量累積と引取量累積との差を小さくする（最小とする、上下限値を越えないようにする等）という制約を設定するのが好適である。上記制約式を定式化するために、旬毎の引取目量標累積からの溢れ量、不足量の変数を定義する。 In addition, the first blending plan creation device 100 creates a blending plan based on the take-up target amount. As will be described later, a series of steps including optimization (steps S103 to S106) and simulation (S107) can be executed by repeating a plurality of loops. In the first loop, a mathematical model to be described later is set based on the data fetched in step S301, and in the next loop and thereafter, the mathematical model is set by reflecting the simulation result in the simulator. For example, the following conditions are taken into account when setting the mathematical model for taking-up target quantity constraints: The take-up quantity to be optimized (incoming quantity) must not deviate from the take-up target quantity by more than a certain width; (There may also be circumstances where a given issue is not picked up for a given period). Here, as a method for formulating a constraint that the take-up amount does not deviate from the take-up target amount by a certain width or more, as shown in FIG. 8A, for example, as shown in FIG. A method or a configuration is conceivable in which an allowable range is set and the amount of arrival is included in the allowable range as a constraint. However, in that case, for example, if the amount of incoming goods meets the lower limit of the acceptable range but continues to fall below the seasonal pick-up target quantity, the annual cumulative incoming quantity may fall below the annual pick-up target quantity. It is possible. Therefore, as shown in FIG. 8B, the pick-up target amount accumulation and the pick-up amount accumulation from the planning start date to the applicable season are calculated every season (or every month), and the difference between the pick-up target amount accumulation and the pick-up amount accumulation is calculated. It is preferable to set a constraint to make it smaller (minimize, not to exceed the upper and lower limits). In order to formulate the above constraint equation, variables for overflow and deficiency from the accumulation of the picked-up metric for each season are defined.
Figure JPOXMLDOC01-appb-M000011
Figure JPOXMLDOC01-appb-M000011
　また、各旬の引取量累積の変数を定義する。 Also, define a variable for the accumulated amount of each season.
Figure JPOXMLDOC01-appb-M000012
Figure JPOXMLDOC01-appb-M000012
　まず、各銘柄の引取量累積を表す制約式は下記の（式１２）と表される。つまり、引取量累積は、立案開始日から当該旬までの期間に入荷する量の合計となる。 First, the constraint equation that represents the cumulative amount received for each issue is expressed as (Equation 12) below. That is, the accumulated amount of collection is the total amount received in the period from the planning start date to the season.
Figure JPOXMLDOC01-appb-M000013
Figure JPOXMLDOC01-appb-M000013
　各銘柄の引取目標量累積と溢れ量、不足量との関係を表す制約式は下記の（式１３）と表される。つまり、引取累積量から溢れ量を引くか、或いは不足量を足すと引取目標累積量と一致する。ここで、引取累積量と引取目標累積量は近い量を取る程好適な計画であるため、この溢れ量、及び不足量は少ない程良い。上記理由のため、後述するように、この溢れ量、及び不足量は、目的関数の項目として追加され、最適化によってミニマム化される。 制約 The constraint equation that expresses the relationship between the accumulation target amount accumulation for each brand, the overflow amount, and the shortage amount is expressed as (Equation 13) below. That is, when the overflow amount is subtracted from the take-up cumulative amount or the shortage amount is added, it coincides with the take-up target cumulative amount. Here, the plan is such that the closer the take-up cumulative amount and the take-up target cumulative amount are, the smaller the overflow amount and the shortage amount are better. For the above reason, as described later, the overflow amount and the shortage amount are added as items of the objective function, and are minimized by optimization.
Figure JPOXMLDOC01-appb-M000014
Figure JPOXMLDOC01-appb-M000014
　なお、上述した需給バランス制約は一例であり、他の制約に替えたり、他の制約を加えたりしてもよい。 Note that the above-described supply-demand balance constraint is an example, and other constraints may be substituted or other constraints may be added.
（７）配合計画の性状制約を数式モデルに設定（図３の基本構成図の性状モデル設定部３１４、図４の線形化部３５７ａを含む性状モデル設定部３５７、図５のステップＳ３０７、Ｓ３０７ａ）
　入力データ取込み部３５１により取り込まれたデータの全部又は一部に基づき、設定された最適化期間及び時間精度を用いて、性状制約を数式モデルに設定する。鉄鉱石の配合計画を作成する場合に用いられる原材料の性状としては、例えば、以下のものが挙げられる：鉄分、SiO2、Al2O3、SiO2、等。石炭の配合計画を作成する場合の性状としては、例えば、以下のものが挙げられる：ＣＳＲ（熱間反応後強度）、ＤＩ（コークス強度）、ＶＭ（揮発分）、膨張圧等。これらの性状が、要求される性状制約を満たす必要がある。混合後の性状モデルの一例を（式１４）に示した。なお、（式１４）では下限値Ｓを有する例を示すが、上限値を有する場合や、上限値及び下限値の両方を有する場合もありうる。
　　ｆ（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓ・・・（式１４）
　　　ｘA～ｘN：原材料（銘柄）Ａ～Ｎの配合割合
　　　Ｓ：下限値（定数） (7) Setting the property constraint of the blending plan in the mathematical model (the property model setting unit 314 in the basic configuration diagram in FIG. 3, the property model setting unit 357 including the linearization unit 357 a in FIG. 4, steps S 307 and S 307 a in FIG. 5)
Based on all or part of the data fetched by the input data fetching unit 351, the property constraint is set in the mathematical model using the set optimization period and time accuracy. Examples of the properties of the raw materials used in preparing the iron ore composition plan include the following: iron, SiO 2 , Al 2 O 3 , SiO 2 , and the like. Examples of properties when preparing a coal blending plan include the following: CSR (strength after hot reaction), DI (coke strength), VM (volatile matter), expansion pressure, and the like. These properties must satisfy the required property constraints. An example of the property model after mixing is shown in (Formula 14). In addition, although (Formula 14) shows an example having the lower limit value S, it may have an upper limit value or may have both an upper limit value and a lower limit value.
f (x A , x B , x C ,..., x N ) ≧ S (Expression 14)
x A to x N : Mixing ratio of raw materials (brands) A to N S: Lower limit (constant)
　ここで、多くの性状について、性状モデルに含まれる数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）は、下式（式１５）に示すように、配合割合に対して線形となる。
　　ｆ（ｘA、ｘB、ｘC、・・・、ｘN）
　　＝ＷA×ＸA＋ＷB×ＸB＋・・・＋ＷN×ＸN・・・（式１５）
        ＷA～ＷN：銘柄毎の銘柄iに含まれる当該成分の性状
　例えば、SiO2に関して、銘柄Ａの配合割合が４０％、銘柄ＡのSiO2成分が１%、銘柄Ｂの配合割合が６０％、銘柄ＢのSiO2成分が２%の条件で混合した場合、混合後のSiO2成分の性状は、１×０．４＋２×０．６＝１．６％となる。 Here, for many properties, the formula f (x A , x B , x C ,..., X N ) included in the property model is based on the blending ratio as shown in the following formula (Formula 15). It becomes linear.
f (x A , x B , x C ,..., x N )
= W A × X A + W B × X B +... + W N × X N (Equation 15)
W A to W N : Properties of the component included in the brand i for each brand For example, regarding SiO 2 , the blending ratio of the brand A is 40%, the SiO 2 component of the brand A is 1%, and the blending ratio of the brand B is 60 %, When the SiO 2 component of the brand B is 2%, the property of the SiO 2 component after mixing is 1 × 0.4 + 2 × 0.6 = 1.6%.
　ところが、性状によっては、その性状を表す数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）が非線形となることがある。この場合、次に述べるように、線形化部３５７ａで、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に代えて線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）を導入して数式モデルを定式化する。 However, depending on the properties, the mathematical expression f (x A , x B , x C ,..., X N ) representing the properties may be nonlinear. In this case, as described below, the linearizing unit 357a replaces the nonlinear mathematical expression f (x A , x B , x C ,..., X N ) with a linear mathematical expression f ′ (x A , x B , X C ,..., X N ) to formulate the mathematical model.
　線形化部３５７ａでの処理について説明する。ある性状を表す数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）が非線形である場合、それに代えて線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）を導入する。この線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）は、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）の下限をなすもの、すなわち（式１６）の関係が成立するものを考える。なお、（式１６）は常に成立する必要はなく、必要な範囲で成立していればよい。
　　ｆ（ｘA、ｘB、ｘC、・・・、ｘN）≧ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）・・・（式１６） Processing in the linearization unit 357a will be described. If the formula f (x A , x B , x C ,..., X N ) representing a certain property is nonlinear, a linear formula f ′ (x A , x B , x C ,. , X N ). This linear formula f ′ (x A , x B , x C ,..., X N ) forms the lower limit of the nonlinear formula f (x A , x B , x C ,..., X N ). Things that satisfy the relationship of (Equation 16) are considered. Note that (Equation 16) does not always need to be satisfied, and only needs to be satisfied within a necessary range.
f (x A , x B , x C ,..., x N ) ≧ f ′ (x A , x B , x C ,..., x N ) (Expression 16)
　例えば線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）として、（式１７)に示す加重平均を考える。加重平均は、単一銘柄を１００％使用した場合の性状を非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）から求め、配合割合を乗算して、使用銘柄分足し合わせた値である。
　　加重平均製鉄所＝Σ[配合割合（＝使用量（製鉄所、銘柄）／使用量合計（製鉄所））×単一銘柄１００％時性状銘柄]・・・（式１７） For example, a weighted average shown in (Expression 17) is considered as a linear expression f ′ (x A , x B , x C ,..., X N ). The weighted average is obtained by using the nonlinear formula f (x A , x B , x C ,..., X N ) to determine the properties when 100% of a single brand is used, and multiplying by the blending ratio. It is the value added together.
Weighted average steelworks = Σ [mixing ratio (= amount used (steelworks, brands) / total amount used (steelworks)) x single brand 100% time brands] (Equation 17)
　説明を簡単にするため、銘柄Ａの配合割合が９０％、銘柄Ｃの配合割合が１０％の例を考える。この場合、線形の数式ｆ´（９０、０、１０、・・・、０）となる加重平均は、下式で表される。
　　ｆ´（９０、０、１０、・・・、０）
　　＝０．９×ｆ（１００、０、・・・、０）＋０．１×ｆ（０、０、１００、・・・０） To simplify the explanation, consider an example in which the blending ratio of brand A is 90% and the blending ratio of brand C is 10%. In this case, a weighted average that is a linear mathematical expression f ′ (90, 0, 10,..., 0) is represented by the following expression.
f ′ (90, 0, 10,..., 0)
= 0.9 x f (100, 0, ..., 0) + 0.1 x f (0, 0, 100, ... 0)
　過去の実績等からこの加重平均が（式１６）を満たすことがわかっていれば、この加重平均を線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）として利用することができる。すなわち、加重平均≧Ｓを制約とすれば、（式１４）が成立するものとみなすことで、定式化できる可能性が得られる。 If it is known from the past results that this weighted average satisfies (Equation 16), this weighted average is used as a linear equation f ′ (x A , x B , x C ,..., X N ). can do. That is, if weighted average ≧ S is a constraint, it is possible to formulate by assuming that (Equation 14) holds.
　線形化部３５７ａでは、（式１４）´に示すように、線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）に対する下限値として、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に対する下限値Ｓよりも小さな仮下限値Ｓ´＝Ｓ－ｓ（ｓ：オフセット値）を設定して、数式モデルを設定する。
　　ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓ´・・・（式１４）´ In the linearization unit 357a, as shown in (Expression 14) ′, as a lower limit value for the linear expression f ′ (x A , x B , x C ,..., X N ), the nonlinear expression f (x A , X B , x C ,..., X N ), a temporary lower limit value S ′ = S−s (s: offset value) smaller than the lower limit value S is set, and a mathematical model is set.
f ′ (x A , x B , x C ,..., x N ) ≧ S ′ (Expression 14) ′
　以上は、混合後の性状制約が下限値を有する場合を例に説明した。なお、上述した性状制約は一例であり、他の制約に替えたり（混合後の性状制約が上限値を有する場合を含む）、他の制約を加えたりしてもよい。 The above is an example in which the property constraint after mixing has a lower limit. The property constraints described above are merely examples, and other constraints may be substituted (including cases where the property constraint after mixing has an upper limit), or other constraints may be added.
　以上説明した需給バランスモデル設定部３５６（図３の需給バランスモデル設定部３１３）及びステップＳ３０６、並びに、性状モデル設定部３５７（図３の性状モデル設定部３１４）及びステップＳ３０７、Ｓ３０７ａが、本発明でいう第１の配合計画作成部の数式モデル設定部及びそれによる処理例である。 The supply / demand balance model setting unit 356 (supply / demand balance model setting unit 313 in FIG. 3) and step S306, the property model setting unit 357 (properties model setting unit 314 in FIG. 3), and steps S307 and S307a described above are included in the present invention. It is the numerical formula model setting part of the 1st mixing | blending plan preparation part said, and the process example by it.
（８）固定化抽出処理（図４の固定化抽出処理部３５８、図５のステップＳ３０８）
　図９に示すように、配船計画の項目である積港、積銘柄、積量、揚港、揚銘柄、揚量のうち固定化されているもの、すなわち変更できないものを抽出する。予め与えられる条件によって、各傭船に対して「積港」及び「積銘柄」が固定化されている場合は、船舶別・積港別・揚港別フレート（図１５を参照）を用いる。つまり、これらの傭船は原材料を積載する傭船が決定されているため、船舶別・積港別・揚港別フレートを用いることで、最適化によって原材料を入荷する製鉄所が決定された時点で、正確な輸送費用計算が可能となる。 (8) Immobilization extraction processing (immobilization extraction processing unit 358 in FIG. 4, step S308 in FIG. 5)
As shown in FIG. 9, items that are fixed, that is, those that cannot be changed, are extracted from among the shipping port items, loading brands, loadings, landing ports, lifting brands, and lifting amounts, which are items of the ship allocation plan. When the “loading port” and “loading brand” are fixed for each dredger according to the conditions given in advance, the freight rate for each ship, each loading port, and each lifting port (see FIG. 15) is used. In other words, since these dredgers have been determined to be loaded with raw materials, by using the freight by ship, by port, by port, and when the steelworks that will receive the raw materials is determined by optimization, Accurate transportation cost calculation is possible.
　また、いずれの項目も固定化されていない場合や、「積港」だけが固定化されている場合は、銘柄別・揚港別見做しフレートを用いる。つまり、積港と積銘柄が決定されていない場合には、当該船舶に関する積港と積銘柄自体を変更可能にすることで、後述の最適化を用いて、当該積港と積銘柄より輸送費用の安い積港と積銘柄の有無を検討することが可能となる。この場合は、銘柄別・揚港別見做しフレートを用いることで当該船舶に関して、当該積港と積銘柄より輸送費用の安い積港と積銘柄に、当該船舶の積港と積銘柄を変更させた計画を後述する最適化により評価、検討させる。これにより輸送費用のより安い計画を作成することを可能としている。なお、同一の傭船に関しては、固定化が最もされていないレコードの状態をこの傭船の固定化状況と考える。この固定化抽出処理は、図５に示したタイミングである必要はなく、例えば配合計画作成を開始するときに行われるようにしてもよい。 Also, if none of the items are fixed, or if only “Sekiko” is fixed, use the freight rate by brand / shipping port. In other words, when the loading port and the brand name have not been determined, the shipping port and the loading name for the ship can be changed, so that the shipping cost can be changed from the loading port and the loading brand using the optimization described later. It is possible to examine whether there are cheap shipping ports and brands. In this case, by using the freight rate by brand / shipping port, the ship's port and the brand of the ship are changed to the port and the brand whose transportation cost is lower than that of the ship and the brand. The planned plan is evaluated and examined by optimization described later. This makes it possible to create a plan with lower transportation costs. For the same dredger, the state of the record that is not fixed most is considered as the anchoring state of this dredger. This immobilization extraction process does not need to be at the timing shown in FIG. 5, and may be performed, for example, when the formulation plan creation is started.
　ここで、本実施形態は、配船計画が存在しない場合、つまり立案開始日以降の配船計画が全く立てられておらず、最初から配合、配船計画を作成して行く場合を例にしてあるため、上記固定化の対象となる船舶情報は存在しない。しかし、本発明はこれを変形した実施形態にも適用できる。例えば、立案開始日から３ヶ月分の配合・配船計画を立案し、１月計画した後に、２ヶ月分は前回立案を参考にし、１ヶ月を全く新規に作成することで、新たな３ヶ月分の配合・配船計画を立案する、等のローリングを行いながら、配合・配船計画が立案されてもよい。このような運用においては、配合計画を立てる際には、計画作成期間の立案開始日に近い日付の一部に対して、配船計画が存在し、またその傭船の一部が固定化されており、計画作成期間の上記に対する残りの期間に対しては、配船計画が全く存在しない。この変形した実施形態において、配合計画を実施する際には、固定化対象となる船舶が存在することとなる。このような実施形態においては、上記固定化抽出処理を用いることで、既存の条件を尊重しつつ、配船及び配合計画の漸次向上を図ることができる。 Here, the present embodiment is an example in which there is no ship allocation plan, that is, no ship allocation plan after the planning start date has been prepared at all, and a formulation and a ship allocation plan are created from the beginning. Therefore, there is no ship information to be fixed. However, the present invention can also be applied to a modified embodiment. For example, after formulating a blending / shipping plan for 3 months from the planning start date, planning for 1 month, and creating 2 months for a new month with reference to the previous planning, a new 3 months A blending / shipping plan may be made while rolling such as making a blending / shipping plan for the minute. In such operations, when formulating a recipe, there is a ship allocation plan for a part of the date close to the planning start date in the planning period, and a part of the dredger is fixed. There is no ship allocation plan for the rest of the plan creation period. In this modified embodiment, when carrying out the blending plan, there is a ship to be immobilized. In such an embodiment, by using the above-described immobilized extraction process, it is possible to gradually improve ship allocation and blending plans while respecting existing conditions.
（９）配合計画数式モデルを目的関数に基づいて最適化（図３の計画部３１５、図４の配合計画求解部３５９、図５のステップＳ３０９）
　上記のように設定された線形及び整数制約式でなる需給バランスモデル、性状モデルを併せて配合計画数式モデルとし、予め構築した目的関数に基づきＬＰ（線形計画法）、ＭＩＰ（混合整数計画法）、ＱＰ（２次計画法）等の数理計画法により最適化問題として問題を解くことにより、最適な使用量、入荷量を計算する。 (9) Optimize formulation formula mathematical model based on objective function (planning unit 315 in FIG. 3, formulation plan solution unit 359 in FIG. 4, step S309 in FIG. 5)
Combined supply and demand balance model and property model consisting of linear and integer constraint equations set as described above are combined into a formulation plan mathematical model, and LP (Linear Programming) and MIP (Mixed Integer Programming) based on pre-established objective functions By solving the problem as an optimization problem by a mathematical programming method such as QP (quadratic programming method), the optimum usage amount and arrival amount are calculated.
　ここでは、目的関数に関して線形式を用いた場合の例を示す。本実施形態では、費用（原材料の購入費用及び輸送費用）のミニマム化を目的としており、目的関数Ｊの一例を（式１７）に示す。目的関数を用いて求解するに際して、購入費用情報及びステップＳ３０８において設定された輸送費用情報を用いる。 Here, an example of using the linear format for the objective function is shown. In this embodiment, the purpose is to minimize costs (raw material purchase costs and transportation costs), and an example of an objective function J is shown in (Equation 17). When solving using the objective function, the purchase cost information and the transportation cost information set in step S308 are used.
Figure JPOXMLDOC01-appb-M000015
 ここで、本実施形態は、第１の配合計画では、配船計画が存在しない場合、つまり立案開始日以降の配船計画が全く立てられておらず、最初から配合、配船計画を作成して行く場合を例にしてあるため、上記固定化の対象となる船舶情報は存在しない。このため、（式１７）において、船舶別・積港別・揚港別フレートに関する項（右辺第２項）は存在しないこととなる。
Figure JPOXMLDOC01-appb-M000015
 Here, in the present embodiment, when there is no ship allocation plan in the first combination plan, that is, no ship allocation plan after the start date of planning is made at all, and a mix and ship allocation plan is created from the beginning. As an example, there is no ship information to be fixed. For this reason, in (Equation 17), there is no term (second term on the right side) regarding the freight by ship, by port, and by port.
　また、第１の配合計画作成装置１００では、原材料の入荷量と引取目標量との関係、すなわち最適化する原材料の入荷量が引取目標量から一定幅以上離れないことを目的としている。
　例えば、旬単位の入荷量と旬毎の引取目標量との差のミニマム化を目的とした目的関数を構築する。或いは、引取量累積（入荷量累積）と引取目標量累積の差のミニマム化を目的とした目的関数を構築する。具体的には、各銘柄の入荷量を旬単位（或いは月単位）に集計し、それまでの累積を考える（入荷量累積）。また、各銘柄の引取目標量を旬単位（或いは月単位）に集計し、それまでの累積を目標値として設定する（引取目標量累積）。そして、入荷量累積と引取目標量累積の差のミニマム化を目的とするよう目的関数を構築する。つまり、引取目標量累積及び引取量累積を考え、引取目標量累積と引取量累積との差を小さくすることも目的としている。本実施例では、累積を考え、旬毎の引取目標累積量からの溢れ量、不足量の合計量をミニマム化する項目を目的関数に追加する。 In addition, the first blending plan creation device 100 is intended to prevent the relationship between the amount of raw material received and the take-up target amount, that is, the amount of raw material to be optimized does not deviate from the take-up target amount by more than a certain width.
For example, an objective function is constructed for the purpose of minimizing the difference between the amount of arrival in seasonal units and the target amount of collection in each season. Alternatively, an objective function is constructed for the purpose of minimizing the difference between the collection amount accumulation (arrival amount accumulation) and the collection target amount accumulation. Specifically, the amount of stock received for each brand is tabulated in season (or monthly) and the accumulation up to that point is considered (cumulative amount of stock). In addition, the collection target amount of each brand is tabulated in seasonal units (or monthly units), and the accumulation up to that time is set as the target value (collection target amount accumulation). Then, an objective function is constructed so as to minimize the difference between the arrival amount accumulation and the pickup target amount accumulation. In other words, taking into account take-up target amount accumulation and take-up amount accumulation, the object is also to reduce the difference between the take-up target amount accumulation and the take-up amount accumulation. In this embodiment, considering the accumulation, an item for minimizing the sum of the overflow amount and the deficiency amount from the seasonal collection target accumulation amount is added to the objective function.
Figure JPOXMLDOC01-appb-M000016
Figure JPOXMLDOC01-appb-M000016
　なお、（式１７）、（式１８）は目的関数の一例であり、他の目的関数に替えたり、他の目的関数を加えたりしてもよい。 (Equation 17) and (Equation 18) are examples of objective functions, and other objective functions may be substituted or other objective functions may be added.
　例えば、与えた目標とする配合割合に近い配合割合に配合計画を近づける必要があり、更に前日の配合割合とその翌日の配合割合が大きく乖離することがない配合計画を作成する必要がある場合は、使用目標量からの溢れ量、不足量、及び当該日の使用量と当該日前日の使用量との差をミニマム化する項目を目的関数に追加する。 For example, when it is necessary to bring the blending plan close to the blending ratio close to the target blending ratio given, and when it is necessary to create a blending plan in which the blending ratio of the previous day and the blending ratio of the next day do not greatly deviate Then, an overflow amount from the target usage amount, a shortage amount, and items for minimizing a difference between the usage amount on the day and the usage amount on the day before are added to the objective function.
Figure JPOXMLDOC01-appb-M000017
Figure JPOXMLDOC01-appb-M000017
　以上の設定した式（数式モデル）を混合整数計画法にて解くことにより、需給バランスモデル、性状モデルを併せた配合計画数式モデルに対する最適解が得られる。 By solving the above set formula (formula model) by the mixed integer programming method, an optimal solution for the blending plan formula model combining the supply and demand balance model and the property model can be obtained.
　以上説明した配合計画求解部３５９（計画部３１５）及びステップＳ３０９が、本発明でいう第１の配合計画作成部の最適化計算部及びそれによる処理の例である。 The above-described blending plan solution unit 359 (planning unit 315) and step S309 are examples of the optimization calculation unit of the first blending plan creation unit and the processing by it in the present invention.
（１０）最適化計算による求解結果の判定（図４の求解結果判定部３６０、図５のステップＳ３１０、Ｓ３１１）
　（式１４）´を用いた最適化計算による求解結果が、非線形の数式を含む数式モデルｆ（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓを満たすか否かを判定する。その結果、非線形の数式を含む数式モデルｆ（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓを満たせば、この求解結果を、後述する性状シミュレータ３６２に対する計算指示としてシミュレーションを実行させる。上記求解結果が非線形の数式を含む数式モデルｆ（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓを満たさなければ、線形の数式を含む数式モデルｆ´（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓ´を調整する（図５のステップＳ３１１）。具体的には、仮下限値Ｓ´を微増させる。 (10) Determination of solution result by optimization calculation (solution result determination unit 360 in FIG. 4, steps S310 and S311 in FIG. 5)
It is determined whether the solution obtained by the optimization calculation using (Expression 14) ′ satisfies a mathematical model f (x A , x B , x C ,..., X N ) ≧ S including a nonlinear mathematical expression. To do. As a result, if a mathematical model f (x A , x B , x C ,..., X N ) ≧ S satisfying a non-linear mathematical formula is satisfied, the solution result is simulated as a calculation instruction to the property simulator 362 described later. Let it run. If the solution result does not satisfy the mathematical model f (x A , x B , x C ,..., X N ) ≧ S including a nonlinear mathematical formula, the mathematical model f ′ (x A , x B , x C ,..., X N ) ≧ S ′ is adjusted (step S311 in FIG. 5). Specifically, the temporary lower limit S ′ is slightly increased.
　図１０は、ステップＳ３０７～Ｓ３１０の処理、すなわち非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に代えて線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）を導入したときの処理を示すフローチャートである。ステップＳ４０１において、需給バランスモデル、性状モデル（非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に代えて線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）を導入して構築したもの）、目的関数Jに基づいて最適化計算を実行する。 10, the processing of steps S307 ~ S310, i.e. non-linear equation f (x A, x B, x C, ···, x N) in place of the linear equation f'(x A, x B, x C ,..., X N ) are flowcharts showing processing. In step S401, the supply-demand balance model, texture model (nonlinear equation f (x A, x B, x C, ···, instead of x N) linear equations f'(x A, x B, x C, .., X N ) are introduced), and the optimization calculation is executed based on the objective function J.
　この場合に、（式１４）´に示したように、線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）に対する下限値として、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に対する下限値Ｓよりも小さな仮下限値Ｓ´＝Ｓ－ｓ（ｓ：オフセット値）を設定する。 In this case, as shown in (Expression 14) ′, as the lower limit value for the linear expression f ′ (x A , x B , x C ,..., X N ), the nonlinear expression f (x A , A temporary lower limit value S ′ = S−s (s: offset value) smaller than the lower limit value S for x B , x C ,..., x N ) is set.
　次にステップＳ４０２において、線形の数式を含む数式モデルｆ´（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓ´を用いた最適化計算による求解結果が、非線形の数式を含む数式モデルｆ（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓを満たすか否かを判定する。すなわち、ステップＳ４０１の最適化計算による求解結果（各銘柄Ａ～Ｎの使用量（配合割合））を（式１４）に代入し、（式１４）が成立するか否かを判定する。 Next, in step S402, the solution obtained by the optimization calculation using the mathematical model f ′ (x A , x B , x C ,..., X N ) ≧ S ′ including the linear mathematical formula is a nonlinear mathematical formula. It is determined whether or not the included mathematical model f (x A , x B , x C ,..., X N ) ≧ S is satisfied. That is, the result of the optimization calculation in step S401 (the usage amount (mixing ratio) of each brand A to N) is substituted into (Expression 14), and it is determined whether (Expression 14) is satisfied.
　ステップＳ４０２の結果、（式１４）が成立すれば、本処理を終了する（図１０のステップＳ４１２に移行する）。それに対して、（式１４）が成立しなければ、ステップＳ４０３に進んで、仮下限値Ｓ´を予め設定された増減幅で微増させて、再度ステップＳ４０１の処理を実行する。すなわち、（式１４）が成立するまで、仮下限値Ｓ´を微増させて、最適化計算による求解を繰り返す収束計算を実行する。 As a result of Step S402, if (Equation 14) is established, this process is terminated (the process proceeds to Step S412 in FIG. 10). On the other hand, if (Equation 14) does not hold, the process proceeds to step S403, where the temporary lower limit S ′ is slightly increased by a preset increase / decrease range, and the process of step S401 is executed again. That is, the convergence calculation is repeated until the provisional lower limit S ′ is slightly increased and the solution by the optimization calculation is repeated until (Equation 14) is satisfied.
　なお、本実施形態では、混合後の性状制約が下限値を有する場合を例にして説明したが、上限値を有する場合も同様である。この場合、線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）は、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）の上限をなすものを考える。また、ステップＳ４０１では、線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）に対する上限値として、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に対する上限値よりも大きな仮上限値を設定する。 In the present embodiment, the case where the property constraint after mixing has a lower limit value has been described as an example, but the same applies to the case where the property constraint has an upper limit value. In this case, the linear mathematical formula f ′ (x A , x B , x C ,..., X N ) is the upper limit of the nonlinear mathematical formula f (x A , x B , x C ,..., X N ). Think about what makes it. In step S401, as an upper limit value for the linear mathematical formula f ′ (x A , x B , x C ,..., X N ), the nonlinear mathematical formula f (x A , x B , x C ,. , X N ) is set to a temporary upper limit value that is larger than the upper limit value.
（１１）求解した解に基づいて在庫推移をシミュレーション（図３の在庫推移シミュレータ３１１、図４の在庫推移シミュレータ３６１、図５のステップＳ３１２）
　上記配合計画数式モデルに対する解、及び、入力データ取込み部３５１により取り込まれたデータの全部又は一部に基づいて、配合の全部或いは一部を対象として、設定した計画確定期間分について、設定した計画作成精度で、シミュレーションを実行する。このシミュレーションでは、配合計画数式モデルには組込むことができなかった制約条件、例えば一定の規則に基づかない条件など、定式化が難しいもの、及び、操業のルール等も組み込んでシミュレートする。このことで、配合計画数式モデルに対する求解結果として出された解を実操業で問題なく使用可能な配合計画に変更する。これにより、実操業で求められる時間精度と、実操業に求められる細かな制約まで考慮した配合計画の立案が可能となる。 (11) Simulation of inventory transition based on the obtained solution (inventory transition simulator 311 in FIG. 3, inventory transition simulator 361 in FIG. 4, step S312 in FIG. 5)
Based on the solution for the above-mentioned blending plan mathematical model and all or part of the data fetched by the input data fetching unit 351, the plan set for the set plan fixed period for all or part of the blend Run the simulation with creation accuracy. In this simulation, a simulation is performed by incorporating restrictions that could not be incorporated into the formulation planning mathematical model, such as conditions that are difficult to formulate, such as conditions that are not based on certain rules, and operation rules. As a result, the solution obtained as a solution to the blending plan mathematical model is changed to a blending plan that can be used without problems in actual operation. This makes it possible to formulate a blending plan that takes into account the time accuracy required in actual operation and the fine restrictions required in actual operation.
　また、数式モデルでは取扱うことが難しい制約の一例として、配合割合が変わった場合の設備の段取りに掛かる段取時間等をシミュレーションに取り込み、正確にシミュレートすることで、実操業に求められる細かな制約まで考慮した配合計画の立案が可能となる。 In addition, as an example of a constraint that is difficult to handle with a mathematical model, the time required for equipment setup when the blending ratio is changed is taken into the simulation, and the detailed simulation required for actual operation It is possible to create a blending plan that takes into account the constraints.
（１２）求解した解に基づいて性状をシミュレーション（図３の性状シミュレータ３１２、図４の性状シミュレータ３６２、図５のステップＳ３１３）
　上記配合計画数式モデルに対する解、在庫推移シミュレータ３６１によりシミュレーションされた在庫推移、及び、入力データ取込み部３５１により取り込まれたデータの全部或いは一部に基づいて、配合の全部或いは一部を対象として、設定した計画確定期間分について、設定した計画作成精度で、性状のシミュレートをおこなう。シミュレーションの結果として、原材料の混合後の性状結果が得られる。このシミュレーションでは、配合計画数式モデルには組み込むことができなかった制約条件、操業のルール等も組み込んでシミュレートすることで、配合計画数式モデルに対する求解結果として出された解を実操業で問題なく使用可能な配合計画に変更する。これにより、実操業で求められる時間精度と、実操業に求められる細かな制約まで考慮した配合計画の立案が可能となる。 (12) Simulate the properties based on the solved solution (the property simulator 312 in FIG. 3, the property simulator 362 in FIG. 4, and step S313 in FIG. 5)
Based on the solution to the blending plan mathematical model, the inventory transition simulated by the inventory transition simulator 361, and all or part of the data captured by the input data capturing unit 351, all or part of the composition is targeted. For the set plan finalization period, the properties are simulated with the set plan creation accuracy. As a result of the simulation, a property result after mixing the raw materials is obtained. In this simulation, by incorporating simulation conditions including constraints, operation rules, etc. that could not be incorporated into the formulation planning formula model, the solution obtained as a solution result for the formulation planning formula model can be used without problems in actual operation. Change to a usable recipe. This makes it possible to formulate a blending plan that takes into account the time accuracy required in actual operation and the fine restrictions required in actual operation.
　以上説明した在庫推移シミュレータ３６１（在庫推移シミュレータ３１１）及びステップＳ３１２、並びに、性状シミュレータ３６２（性状シミュレータ３１２）及びステップＳ３１３が、本発明でいう第１の配合計画作成部のシミュレータ及びそれによる処理の例である。 The inventory transition simulator 361 (inventory transition simulator 311) and step S312, and the property simulator 362 (property simulator 312) and step S313 described above are the simulators of the first blending plan creation unit and the processes performed thereby in the present invention. It is an example.
（１３）配合計画の確定（図４の確定部３６３、図５のステップＳ３１４）
　上記在庫推移シミュレーション、性状シミュレーションにより導き出された配合計画のうちで設定した計画確定期間分を確定する。図７に示すように、本実施形態では計画確定期間を１日と設定しているので、作成した配合計画の最初の１日分を確定する。作成した配合計画のうちで上記計画確定期間に入らなかった部分については、その計画は確定せずに破棄する。 (13) Confirmation of blending plan (confirmation unit 363 in FIG. 4, step S314 in FIG. 5)
The plan decision period set in the combination plan derived by the inventory transition simulation and the property simulation is confirmed. As shown in FIG. 7, in this embodiment, since the plan determination period is set to one day, the first one day of the created formulation plan is fixed. Of the created blending plan, the portion that has not entered the plan finalization period is discarded without being finalized.
（１４）計画作成期間分、或いは計画確定期間分の計画が確定したか判定（図４の判定部３６４、図５のステップＳ３１５）
　このステップの実行時点までに確定した計画確定期間が予め設定した計画作成期間の全体を含んでいるかを判断する。本実施形態では、計画作成期間が１０日間であるので、第１０ループで計画を確定した時点で計画確定期間分の計画が確定する。このため第１０ループで計画を確定終了した時点で１０日分の配合計画を作成して、処理を終了する。 (14) Judgment whether the plan for the plan creation period or the plan for the plan confirmation period has been confirmed (determination unit 364 in FIG. 4, step S315 in FIG. 5)
It is determined whether the plan finalization period determined up to the execution time of this step includes the entire preset plan creation period. In this embodiment, since the plan creation period is 10 days, the plan for the plan confirmation period is confirmed when the plan is confirmed in the tenth loop. For this reason, when the plan is finalized in the tenth loop, a blending plan for 10 days is created and the process is terminated.
（１５）立案開始日の更新（図４の更新部３６５、図５のステップＳ３１６）
　このステップの実行時点で確定した計画確定期間が予め設定した計画作成期間の全体を含んでいない場合、上記配合計画のうちで確定した配合計画期間直後の日時を新たな立案開始日として設定する。本実施形態では、図７に示すように、第１ループでは当初１日目０時であった立案開始日を２日目０時に、第２ループでは当初２日目０時であった立案開始日を３日目０時に更新する。 (15) Planning start date update (update unit 365 in FIG. 4, step S316 in FIG. 5)
If the plan decision period determined at the time of execution of this step does not include the entire plan preparation period set in advance, the date and time immediately after the combination plan period determined in the combination plan is set as a new planning start date. In the present embodiment, as shown in FIG. 7, the planning start date that was initially 0 o'clock on the first day in the first loop is 0 o'clock on the second day, and the planning start that was originally 0 o'clock on the second day in the second loop is started. Update the day to 0:00 on the third day.
（１６）配合計画の出力（図４の出力部３６６、図５のステップＳ３１７）
　以上のようにして作成した配合計画は、出力部３６６により、表示部３０３に画面表示されたり、データベース４００を含む外部機器にデータ送信されたりする。 (16) Output of formulation plan (output unit 366 in FIG. 4, step S317 in FIG. 5)
The formulation plan created as described above is displayed on the screen of the display unit 303 by the output unit 366 or is transmitted to an external device including the database 400.
　以上説明した出力部３６６及びステップＳ３１７が、本発明でいう第１の配合計画作成部の出力部及びそれによる処理の例である。 The output unit 366 and step S317 described above are examples of the output unit of the first blending plan creation unit and the processing performed thereby in the present invention.
　以上のように、現在の在庫推移状態に応じて、需給バランス制約、性状制約について、まず所定の最適化期間分について、計画作成時間精度で数式モデルを設定し、設定した配合計画数式モデルを目的関数に基づいて求解し、求解した解に基づいて、在庫推移、混合後の性状をシミュレートし、シミュレーション結果から求められた配合計画のうちで、設定した計画確定期間分を確定し、計画確定期間直後の日時を新たな立案開始日時とすることにより、新たな計画対象期間分の配合計画を確定する一連の処理を順次、予め定めた回数だけ、繰り返して実行する。このことで、所望する計画作成期間分の配合計画を作成することができる。これにより、任意の時間精度を必要とする配合計画を高速かつ詳細に最適化することができ、しかも得られた結果をそのままで実操業に適用できる。 As described above, according to the current inventory transition status, for the supply and demand balance constraint and property constraint, first, a mathematical model is set with the plan creation time accuracy for the predetermined optimization period, Solves based on the function, simulates inventory transition and mixed properties based on the solved solution, confirms the set plan finalization period from the formulation plan obtained from the simulation results, and finalizes the plan By setting the date and time immediately after the period as a new planning start date and time, a series of processes for determining a blending plan for a new planning target period is sequentially and repeatedly executed a predetermined number of times. Thereby, the mixing | blending plan for the plan preparation period desired can be created. This makes it possible to optimize a blending plan that requires an arbitrary time accuracy at high speed and in detail, and to apply the obtained results to actual operations as they are.
　なお、上記実施形態では、図１１に示すように、配合計画は一定の期間（例えば旬）毎に作成される。また、複数の性状α、βについて性状モデルが非線形となることがある。
　図１１において、Ａは性状制約を満たしている（（式１４）が成立している）ことを、Ｂは性状制約を満たしていないことを意味する。すなわち、図１１の例では、性状αについて複数旬（４月上旬及び下旬）で性状違反が発生しており、同様に性状βについて複数旬（４月上旬及び下旬）で性状違反が発生している。 In the above embodiment, as shown in FIG. 11, the blending plan is created every certain period (for example, seasonal). In addition, the property model may be nonlinear with respect to a plurality of properties α and β.
In FIG. 11, A means that the property constraint is satisfied (Equation 14 is satisfied), and B means that the property constraint is not satisfied. In other words, in the example of FIG. 11, there are property violations occurring in multiple seasons (early and late April) for property α, and similarly, property violations occur in multiple seasons (early and late April) for property β. Yes.
　この場合に、各旬及び各性状について別個に図１０で説明した収束計算を行うと、以下の問題が生ずる。具体的にいえば、４月上旬で性状αについて収束計算を行い、続いて性状βについて収束計算を行い、また、４月下旬で性状αについて収束計算を行い、続いて性状βについて収束計算を行うのでは、計算処理に時間がかかってしまう。 In this case, if the convergence calculation described in FIG. 10 is performed separately for each season and each property, the following problems occur. Specifically, the convergence calculation is performed for the property α in early April, the convergence calculation is performed for the property β, the convergence calculation is performed for the property α in late April, and the convergence calculation is performed for the property β. Doing so will take time for the calculation process.
　そこで、対象の旬及び性状についてまとめて図１０で説明した収束計算を行うようにする。例えば４月上旬及び下旬で性状α、βについてまとめて収束計算を行う（図１０のステップＳ４０３で性状α、βの仮下限値の微増（或いは仮上限値の微減）を同時に行う）ことにより、高速化を図ることができる。 Therefore, the convergence calculation described in FIG. 10 is performed by summarizing the target season and properties. For example, by performing convergence calculation for the properties α and β at the beginning and the end of April (by slightly increasing the temporary lower limit values (or slightly decreasing the temporary upper limit value at the same time in step S403 in FIG. 10)), The speed can be increased.
　また、上記実施形態では、図１０のステップＳ４０３で仮下限値Ｓ´を微増（或いは仮上限値を微減）させた後、再度ステップＳ４０１の処理を実行すると説明した。この場合に、収束計算が変化しても設定に変化のない数式モデル、具体的には上述した需給バランスモデルや元々線形の性状モデルは保持しておく。そして、仮下限値を微増（或いは仮上限値を微減）させて再度ステップＳ４０１の処理を実行する場合に、収束計算に従って変化のある数式モデル、具体的には仮下限値を微増させた（或いは仮上限値を微減させた）数式モデルのみ変更するような仕組とすることにより、高速化を図ることができる。 In the above-described embodiment, it has been described that the temporary lower limit value S ′ is slightly increased (or the temporary upper limit value is slightly decreased) in step S403 in FIG. In this case, a mathematical model in which the setting does not change even when the convergence calculation changes, specifically, the above-described supply-demand balance model or an originally linear property model is retained. Then, when the provisional lower limit value is slightly increased (or the provisional upper limit value is slightly decreased) and the process of step S401 is executed again, the mathematical model that changes according to the convergence calculation, specifically, the provisional lower limit value is slightly increased (or Speeding up can be achieved by adopting a structure in which only the mathematical model (with the temporary upper limit value slightly reduced) is changed.
　また、配合計画（例えば使用量（配合割合））として、年次計画、期計画、月次計画といった長期間の計画を立案することが多い。このように長期の配合計画を予め作成し、その配合計画を基準の配合計画とし、第１の配合計画作成装置１００により作成されたより短期の配合計画が、基準となる配合計画から一定幅以上離れないようにすることも重要となる。 Also, in many cases, a long-term plan such as an annual plan, a term plan, or a monthly plan is formulated as a blending plan (for example, usage (mixing ratio)). In this way, a long-term blending plan is created in advance, the blending plan is used as a reference blending plan, and the shorter-term blending plan created by the first blending plan creation device 100 is more than a certain distance away from the reference blending plan. It is also important to avoid it.
　そこで、（式１７）に示したような費用（原材料の購入費用及び輸送費用）に関して構築された目的関数Ｊに加え、予め作成された基準となる配合計画と一定幅以上離れないようにする条件に関して構築された目的関数J´を用いるようにしてもよい。目的関数Ｊ´の一例を（式２０）に示す。
　　Ｊ´＝Σ（｜基準配合割合（銘柄）－配合割合（銘柄、日）｜）→ミニマム化・・・（式２０）
　　基準配合割合：基準となる配合計画における配合比 Therefore, in addition to the objective function J constructed with respect to the costs shown in (Equation 17) (raw material purchase costs and transportation costs), the conditions for keeping a predetermined range or less from the standard formulation plan prepared in advance The objective function J ′ constructed for may be used. An example of the objective function J ′ is shown in (Equation 20).
J ′ = Σ (| reference blending ratio (brand) −blending ratio (brand, date) |) → minimization (Formula 20)
Standard blending ratio: blending ratio in the standard blending plan
　上記例では、月次計画において、期計画を基準となる配合計画として、日々の配合計画を作成する場合の一例を示した。この場合は、配合割合（銘柄、日）と基準配合割との差の銘柄毎、日毎に合計したものをミニマム化する。他の例として、期計画を立案する場合、年次計画を基準となる配合計画として計画を作成しても良い。この場合、月次計画では配合割合（銘柄、月）を決定するとした場合は、配合割合（銘柄、月）と基準配合割との差を銘柄毎、月毎に合計した値をミニマム化する。 In the above example, an example of creating a daily blending plan as a blending plan based on the term plan in the monthly plan is shown. In this case, the sum totaled for each brand and each day of the difference between the blending ratio (brand, day) and the standard blending ratio is minimized. As another example, when planning a term plan, the plan may be created as a blending plan based on the annual plan. In this case, in the monthly plan, when it is determined that the blending ratio (brand, month) is determined, the difference between the blending ratio (brand, month) and the standard blending ratio is summed for each brand and every month.
　なお、基準となる配合計画は、例えば過去の実績に基づいて作成され、その作成手法はどのようなものであってもよい。もちろん、本発明を適用した配合計画作成手法により長期間の計画を予め作成しておき、それを基準となる配合計画としてもよい。 In addition, the mixing | blending plan used as a reference | standard is produced based on the past performance, for example, The production method may be what kind. Of course, a long-term plan may be created in advance by a blending plan creation method to which the present invention is applied, and this may be used as a reference blending plan.
（配船計画作成装置２００）
　配船計画作成装置２００は、データベース４００から、例えば以下のデータを取り込む：原材料の使用予定量（第１の配合計画作成装置１００により作成された配合計画による使用量）、引取目標量、傭船契約の種別の異なる船舶がリストアップされた船舶リスト、船舶リストにリストアップされている船舶の運航状況、原材料の在庫状況、原材料の単価を表す購入費用情報、船舶リストにリストアップされている船舶を利用する場合の輸送費用情報。配船計画作成装置２００は、取り込んだデータに基づいて、例えば３ヶ月（９旬）分の配船計画を作成する。ここで、旬は月を３つに分割した期間の単位を指す。配船計画として、具体的には、連続航海船、不定期船、スポット船に対する積揚地（積揚港）、積揚銘柄、積揚量、寄港順、着岸バース、入出港タイミング、及び雇うべきスポット船の船数と船型（船の最大積載量を基に定義される船舶の大きさ）等を決定する。 (Shipment planning device 200)
The ship allocation plan creation device 200 fetches, for example, the following data from the database 400: scheduled use amount of raw materials (amount used by the blending plan created by the first blending plan creation device 100), take-up target amount, chartering contract Ship list with different types of ships listed, operation status of ships listed in ship list, stock status of raw materials, purchase cost information indicating unit price of raw materials, ship listed in ship list Transportation cost information when used. The ship allocation plan creating apparatus 200 creates a ship allocation plan for, for example, three months (September) based on the acquired data. Here, seasonal refers to the unit of period divided into three months. Specifically, the ship allocation plan includes continuous navigation ships, non-regular ships, landing sites for spot ships (shipping ports), loading brands, loadings, port order, berth berth, entry / exit timing, and hire Determine the number and type of spot ships (the size of the ship defined based on the maximum capacity of the ship), etc.
　ここで、使用予定量は、第１の配合計画作成装置１００により作成された配合計画による使用量である。使用予定量は、入荷量が設定された引取目標量にできるだけ近い量となり、尚且つ輸送費用を安くする条件を考慮して決定されている。このため、第１の配合計画作成装置１００により作成された配合計画に基づく使用量を、配船計画作成装置２００の入力データとして用いることで、配船計画を立てる際にも、輸送費用を考慮せず計画された使用量を用いるより、より安い輸送費用での配船計画の立案が可能となる。 Here, the scheduled usage amount is the usage amount according to the blending plan created by the first blending plan creation device 100. The scheduled use amount is determined in consideration of conditions that make the arrival amount as close as possible to the set take-up target amount and reduce the transportation cost. For this reason, by using the usage amount based on the blending plan created by the first blending plan creation device 100 as input data to the shipping plan creation device 200, the transportation cost is also taken into consideration when making the dispatching plan. It is possible to make a ship allocation plan at a lower transportation cost than using the planned usage amount.
　例えば、性状がほぼ同一の原材料Ｘ，Ｙがあり、揚港（製鉄所）Ａ，Ｂでは原材料Ｘ，Ｙどちらでの使用も可能な場合を考える。揚港Ａに原材料Ｘを輸送する費用が２０＄／トン、原材料Ｙを輸送する費用４０＄／トンであり、揚港Ｂに原材料Ｘを輸送する費用が４０＄／トン、原材料Ｙを輸送する費用２０＄／トンである例を考慮する。この場合、配合計画を作成する際に輸送費用が考慮されていないと、揚港Ａで原材料Ｙ、揚港Ｂで原材料Ｘを使用する計画を立ててしまう畏れがある。しかし、輸送費用の観点からは、揚港Ａで原材料Ｘ、揚港Ｂで原材料Ｙを使用する計画がより好ましい。本実施形態では、上記のような状況を回避することが可能となる。 Suppose, for example, that there are raw materials X and Y having almost the same properties, and that it is possible to use either raw materials X or Y at Yanggang (steelworks) A and B. The cost of transporting the raw material X to the unloading port A is 20 $ / ton, the cost of transporting the raw material Y is 40 $ / ton, the cost of transporting the raw material X to the unloading port B is 40 $ / ton, and the raw material Y is transported. Consider an example where the cost is $ 20 / ton. In this case, if the transportation cost is not taken into consideration when preparing the blending plan, there is a possibility that a plan to use the raw material Y at the unloading port A and the raw material X at the unloading port B may be made. However, from the viewpoint of transportation costs, it is more preferable to use the raw material X at the unloading port A and the raw material Y at the unloading port B. In the present embodiment, the above situation can be avoided.
　図１に示すように、配船計画作成装置２００において、２０１は船舶の運航、積地、揚地での設備、ヤード等を模擬したシミュレータである。シミュレータ２０１は、後述するマクロ最適化部２０２、ミクロ最適化部２０３により決定された入力情報を受け、これに基づいて詳細なシミュレーションを実行する。この入力情報は、連続航海船、不定期船、スポット船に対する積揚地（積揚港）、積揚銘柄、積揚量、寄港順、着岸バース、入出港タイミング、及び雇うべきスポット船の船数と船型（船の最大積載量を基に定義される船舶の大きさ）の情報を含む。本シミュレータは、在庫推移シミュレータ及び船舶運航状況推移シミュレータにより構成される。 As shown in FIG. 1, in the ship allocation plan creating apparatus 200, 201 is a simulator that simulates ship operation, loading and unloading facilities, yards, and the like. The simulator 201 receives input information determined by a macro optimization unit 202 and a micro optimization unit 203, which will be described later, and executes a detailed simulation based on the input information. This input information includes the voyage of vessels, non-regular ships, and spot ships (shipping port), loading brands, amount of loading, arrival order, berthing berth, entry / exit timing, and spot ship to be hired. Contains information on the number and type of ship (the size of the ship defined based on the maximum load capacity of the ship). This simulator is composed of an inventory transition simulator and a ship operation status transition simulator.
　在庫推移シミュレータは、各製鉄所における原材料の在庫推移を計算する。この在庫推移シミュレータでは、各製鉄所の原材料の使用予定量、船舶の原材料の銘柄毎の荷揚げ時刻を考慮し、詳細に原材料の銘柄毎の在庫推移を計算する。例えば、船舶に複数銘柄が積載され、１銘柄を荷揚げした後、２銘柄目を荷揚げする時点で、ヤード能力が溢れていた場合、ヤード上の原材料の在庫量が減り、ヤード能力に余裕が出来るまで時間を空けて荷揚げをする必要が生じる場合がある。このような事情等を考慮して、荷揚げ時刻に対応させて在庫の推移が正確にシミュレートされる。 The inventory transition simulator calculates the inventory transition of raw materials at each steelworks. This inventory transition simulator calculates the inventory transition for each brand of raw material in detail, taking into account the planned use amount of the raw material for each steelworks and the unloading time for each brand of the ship's raw material. For example, if multiple brands are loaded on a ship and one brand is unloaded and then the second brand is unloaded, if the yard capacity is overflowing, the stock amount of raw materials on the yard will be reduced, and the yard capacity can be afforded. It may be necessary to unload at a certain time. Taking such circumstances into consideration, the transition of inventory is accurately simulated in accordance with the unloading time.
　船舶運航状況推移シミュレータは、積揚港の沖着日時（ＥＴＡ：Estimated Time Of Arrival）、積揚港着岸の日時（ＥＴＢ：Estimated Time Of Berthing）、積揚港出港の日時（ＥＴＤ：Estimated Time Of Departure）を含む船舶の運航状況の推移を計算する。この船舶運航状況推移シミュレータでは、荷積能力、荷揚能力の他に、他岸壁に船舶が存在するかどうか（存在する場合には着岸できない）等、他船舶との干渉等も考慮し、詳細に船舶の運航状況をシミュレートする。例えば、船舶の荷揚げに使用するアンローダの基数は、荷揚げする銘柄が積載されているハッチの位置、同一揚港の別バースで荷役している船舶があるか、ないか等を考慮する。この荷揚げに使用するアンローダの基数により荷揚能力が影響される。一例として、１基のアンローダで荷揚げする場合は、１５００ｔ／ｈで１００％能力で荷揚げを行える。また、２基のアンローダの場合は、１５００ｔ／ｈ×２基で７０％能力で荷揚げを行える。上記船舶運航状況推移シミュレータは、これらアンローダ基数等の条件による荷揚能力の変化を、シミュレーションに取り込み、正確にシミュレートする。このことで、実操業に求められる細かな制約まで考慮した、具体的な生産・物流計画の立案を可能とする。 The ship operation status transition simulator includes the arrival date and time (ETA: Estimated Time Of Arrival) of the loading port, the date and time of arrival at the loading port (ETB: Estimated Time Of Berthing), and the date and time of departure from the loading port (ETD: Estimated Time Of) Calculate the transition of ship operation status including Departure). In addition to loading capacity and unloading capacity, this ship operation status transition simulator takes into account interference with other ships, such as whether or not there is a ship on the other quay (if it exists) Simulate ship operation. For example, the number of unloaders used to unload a ship considers the position of the hatch where the brand to be unloaded is loaded, whether or not there is a ship handling at another berth of the same unloading port, and so on. The loading capacity is affected by the number of unloaders used for unloading. As an example, when unloading with one unloader, unloading can be performed with a capacity of 100% at 1500 t / h. In the case of two unloaders, unloading can be performed with a capacity of 70% at 1500 t / h × 2. The above-mentioned ship operation status transition simulator incorporates changes in the unloading capacity due to conditions such as the unloader radix into the simulation and accurately simulates it. This makes it possible to create a specific production / logistics plan that takes into account the fine constraints required for actual operations.
　２０２はマクロ最適化部であり、製鉄所の配合計画（上記の原材料の使用予定量）に支障をきたさないこと、及び、積み出し可能量を守ることを前提に、輸送費用のうちのフレートの合計金額を最も安価にすることを一つの目的として、以下の各条件を決定するように最適化を行う：連続航海船、不定期船、スポット船に対する積揚地（積揚港）、積揚銘柄、積揚量、寄港順、及び雇うべきスポット船の船数と船型（船の最大積載量を基に定義される船舶の大きさ）。マクロ最適化部２０２は、本発明でいう船舶財源リスト作成部として機能する船舶財源リスト作成部２０２ａ、本発明でいう数式モデル設定部として機能する数式モデル設定部２０２ｂ、本発明でいう最適化計算部として機能する最適化計算部２０２ｃを備え、例えば９旬分を旬精度に演算する。 202 is a macro optimization section, which is the sum of freights in transportation costs, assuming that there is no hindrance to the steel mill's blending plan (the amount of raw materials used above) and that the amount that can be shipped is protected. Optimized to determine each of the following conditions, with the objective of making the amount of money the cheapest: Unloading sites (shipping ports), unloading brands for continuous voyages, irregular ships, spot ships , Loading capacity, port order, and the number and type of spot ships to be hired (the size of the ship as defined by the maximum capacity of the ship). The macro optimization unit 202 includes a ship resource list creation unit 202a that functions as a ship resource list creation unit according to the present invention, a formula model setting unit 202b that functions as a formula model setting unit according to the present invention, and an optimization calculation according to the present invention. An optimization calculation unit 202c functioning as a unit is provided, and for example, 9 seasons are calculated with seasonal accuracy.
　２０３はミクロ最適化部であり、マクロ最適化部２０２により最適化された計画において滞船料の合計金額を最も安価にする着岸バース、入出港タイミングを決定するように最適化を行って、シミュレータ２０１に対する指示を算出する。ミクロ最適化部２０３は、本発明でいう数式モデル設定部として機能する数式モデル設定部２０３ａ、本発明でいう最適化計算部として機能する最適化計算部２０３ｂを備え、例えば１旬分を分精度に演算する。 Reference numeral 203 denotes a micro-optimization unit, which performs optimization so as to determine a docking berth and an entry / exit timing at which the total amount of the berthing fee is the lowest in the plan optimized by the macro optimization unit 202. An instruction for 201 is calculated. The micro optimization unit 203 includes a formula model setting unit 203a that functions as a formula model setting unit according to the present invention, and an optimization calculation unit 203b that functions as an optimization calculation unit according to the present invention. Calculate to
　２０４は本発明でいうデータ取込み部として機能するデータ取込み部であり、データベース４００から原材料の使用予定量、引取目標量、傭船契約の種別の異なる船舶がリストアップされた船舶リスト、船舶リストにリストアップされている船舶の運航状況、原材料の在庫状況、原材料の購入費用、船舶リストにリストアップされている船舶を利用する場合の輸送費用、積地での船舶停泊状況、荷積能力状況、設備修理・休止予定、揚地での船舶停泊状況、荷揚能力状況、設備修理・休止予定等のデータを取り込む。 204 is a data acquisition unit that functions as a data acquisition unit according to the present invention. A ship list in which ships with different types of raw material use scheduled amounts, target collection amounts, and chartering contracts are listed from the database 400 is listed in the ship list. Ship operation status, raw material inventory status, raw material purchase cost, shipping cost when using a ship listed in the ship list, ship berth status, loading capacity status, equipment Capture data such as repair / suspension schedule, ship berthing status at landing site, unloading capacity status, facility repair / suspension schedule.
　２０５は本発明でいう出力部として機能する出力部であり、シミュレータ２０１によるシミュレーション結果として作成された配船計画、具体的には、連続航海船、不定期船、スポット船に対する積揚地（積揚港）、積揚銘柄、積揚量、寄港順、着岸バース、入出港タイミング、及び雇うべきスポット船の船数と船型（船の最大積載量を基に定義される船舶の大きさ）を画面表示したり、データベース４００を含む外部機器にデータ送信したりする。 Reference numeral 205 denotes an output unit functioning as an output unit in the present invention, which is a ship allocation plan created as a simulation result by the simulator 201, specifically, a landing site for a continuous voyage ship, an irregular ship, and a spot ship. Unloading port), unloading brand, unloading order, arrival order, berthing berth, arrival / departure timing, number of spot ships to be hired and ship type (the size of the ship defined based on the maximum loading capacity of the ship) Screen display or data transmission to an external device including the database 400 is performed.
　以下、本実施形態に係る配船計画作成装置２００による配船計画作成処理の詳細を説明する。図１２は、配船計画作成装置２００を用いた配船計画作成方法における各処理のステップを説明するためのフローチャートである。本実施形態では、ユーザが設定した立案開始日から３ヶ月（９旬）を計画作成期間として配船計画を作成する。 Hereinafter, the details of the ship assignment plan creation processing by the ship assignment plan creation apparatus 200 according to the present embodiment will be described. FIG. 12 is a flowchart for explaining the steps of each process in the ship assignment plan creation method using the ship assignment plan creation apparatus 200. In the present embodiment, a ship allocation plan is created with a plan creation period of 3 months (9 seasons) from the planning start date set by the user.
（１）データの取込み（ステップＳ１０１）
　配船計画作成装置２００のデータ取込み部２０４は、データベース４００から原材料の使用予定量、引取目標量、契約の種別の異なる船舶がリストアップされた船舶リスト、船舶リストにリストアップされている船舶の運航状況、原材料の在庫状況、原材料の購入費用、船舶リストにリストアップされている船舶を利用する場合の輸送費用、積地での船舶停泊状況、荷積能力状況、設備修理・休止予定、揚地での船舶停泊状況、荷揚能力状況、設備修理・休止予定等のデータを取り込む。 (1) Data acquisition (step S101)
The data acquisition unit 204 of the ship allocation plan creation device 200 includes a ship list in which ships with different raw material usage schedules, take-up target quantities, and contract types are listed from the database 400, and ships listed in the ship list. Operational status, raw material inventory status, raw material purchase cost, transportation cost when using a ship listed in the ship list, ship berthing status at loading site, loading capacity status, facility repair / suspension schedule, lifting Capture data such as ship berthing status, unloading capacity status, facility repair / outage schedule, etc.
　ここで、原材料の使用予定量は、第１の配合計画作成装置１００で作成された配合計画から算出される、計画作成期間における製鉄所（揚地）別、原材料の銘柄別の使用予定量を表す情報である。原材料はその銘柄毎に、品質・性状等に違いがあるため、それぞれ銘柄毎に使用予定量を決め、配合される。 Here, the planned usage amount of raw materials is calculated from the blending plan created by the first blending plan creation device 100, and is the planned usage amount for each steelworks (pump) and raw material brand in the plan creation period. It is information to represent. The raw materials differ in quality, properties, etc. for each brand, so the amount to be used for each brand is determined and blended.
　引取目標量は、山元（積地）別、銘柄別の引取目標量（引取予定量）を表す情報である。各山元とは銘柄毎に例えば年間どれだけの量を引き取るかについて契約しており、それを旬数で割れば旬毎の引取目標量が得られる。この引取目標量に近づけるように、配船することが求められる。ただし、配船計画との関係で、引取目標量からの数万トン程度の上下へのぶれは山元との交渉により、許容範囲内となる。また、契約によっては、所定の銘柄については所定の期間は引取しないといった契約も考えられる。このような契約に関する具体的な情報をデータに含めてもよい。 The collection target amount is information representing the collection target amount (planned collection amount) by Yamamoto (loading place) and by brand. For example, each Yamamoto contracts with each brand to decide how much to take for each year, for example. Dividing it by the number of seasons gives the target amount for each season. It is required to allocate ships so as to approach this take-up target amount. However, in relation to the ship allocation plan, up and down movements of about tens of thousands of tons from the take-over target amount will be within the allowable range through negotiations with Yamamoto. In addition, depending on the contract, there may be a contract in which a predetermined brand is not picked up for a predetermined period. Specific information regarding such a contract may be included in the data.
　船舶リストは、図１３に示すように、契約の種別の異なる船舶、ここでは具体的に連続航海船、不定期船、スポット船をリストアップした情報である。連続航海船の傭船契約は、契約期間において連続航海する契約である。このため、必ず（最優先で）配船することが求められる。不定期船の傭船契約は、契約期間において契約した航海数又は契約した航海期間のみ航海する契約である。このため、契約した航海数又は契約した航海期間内で必ず（最優先で）配船することが求められる。スポット船は、本実施形態の使用、または実行時点では未契約である。連続航海船及び不定期船を配船しても、目標とする引取量を満たせない、或いは揚地の在庫を充足できない場合に、これらスポット船にスポット的な航海を依頼することができる。連続航海船については、取込みデータ中に、傭船コード（船一隻一隻を特定するコード）、契約区分、契約期間（開始日及び終了日）、最大積載量、船名が記載される。不定期船については、傭船コード、契約区分、契約期間（開始日及び終了日）、契約の内容（契約した航海数又は契約した航海期間）、最大積載量、船名が記載される。これら連続航海船及び不定期船は船舶を個別にリストアップしているが、スポット船については、船舶の航行できる地域名と、船舶の船型（船の最大積載量を基に定義される船舶の大きさ）でリストアップし、傭船コード（地域名と大きさが記述される）、契約区分、最大積載量が記載される。 As shown in FIG. 13, the ship list is information that lists ships with different types of contracts, specifically, continuous voyage ships, irregular ships, and spot ships here. A chartering contract for a continuous voyage is a contract for continuous voyage during the contract period. For this reason, it is always necessary to dispatch ships (with the highest priority). A chartering contract for a non-regular ship is a contract that sails only during the contract period or during the contract period. For this reason, the number of contracted voyages or the contracted voyage period must be assigned (with the highest priority). The spot ship is not contracted at the time of use or execution of this embodiment. Even if a continuous voyage ship and a non-regular ship are assigned, if the target take-up amount cannot be satisfied or the stock of the landing site cannot be satisfied, spot voyages can be requested to these spot ships. For continuous voyage vessels, the chart includes the chartering code (code that identifies each ship), contract classification, contract period (start date and end date), maximum loading capacity, and ship name. For irregular ships, charter code, contract category, contract period (start date and end date), contract details (contracted voyage or contracted voyage period), maximum loading capacity, and ship name are described. These continuous cruise ships and irregular ships list ships individually, but for spot ships, the name of the area where the ship can navigate and the ship's ship type (the ship's type defined based on the maximum load capacity of the ship). Listed by size), chartering code (area name and size is described), contract classification, and maximum load capacity are described.
　なお、船型とは、船の最大積載量を基に定義される船舶の大きさを表す。スポット船の船型を表すＰｍａｘはパナマ運河を通過できる船舶（一般にこの船型はパナマックスと呼ばれる）、Ｃａｐｅはケープ岬を通過できる船舶（一般にこの船型はケープサイズと呼ばれる）、ＶＬ（Very Large）はこれらより大きい大型船であることを意味する。ここで、通常パナマックスとは、長さ９００フィート以内、幅１０６フィート以内の船で、最大積載可能量が６万～８万トンクラスの船を指す。また通常ケープサイズとは、最大積載か能力が１５万～１７万トンクラスの船を指す。スポット船については、配船計画を立てる段階で、この航行できる地域と船舶の大きさを基に、必要な船数、船型（船の最大積載量を基に定義される船舶の大きさ）を決定する。配船計画がある程度確定される段階になって、実際の船会社と交渉して、上記船型にマッチする船を契約する手続きが取られる。このため、配船計画を立てる段階では、まず未契約の状態（船会社と交渉する前の段階）で、必要な船数、船型（船の最大積載量を基に定義される船舶の大きさ）を決定することが求められる。 Note that the hull form represents the size of a ship defined based on the maximum load capacity of the ship. Pmax, which represents the ship type of a spot ship, is a ship that can pass through the Panama Canal (generally this ship type is called Panamax), Cape is a ship that can pass Cape Cape (generally this ship type is called Cape size), and VL (Very Large) is It means a larger ship than these. Here, the normal Panamax is a ship that is 900 feet long and 106 feet wide and has a maximum load capacity of 60,000 to 80,000 tons. The normal cape size refers to ships with a maximum capacity or capacity of 150,000 to 170,000 tons. For spot ships, at the stage of making a ship assignment plan, based on this navigable area and the size of the ship, the required number of ships and ship type (the ship size defined based on the maximum load capacity of the ship) decide. At a stage where the ship allocation plan is finalized to some extent, a procedure for negotiating with the actual shipping company and contracting a ship that matches the above-mentioned ship type is taken. For this reason, at the stage of making a ship assignment plan, the number of ships required and the type of ship (the size of the ship defined based on the maximum load capacity of the ship) must be unsigned (before negotiation with the shipping company). ) Is required.
　船舶運航状況は、図１４に示すように、船舶リストにリストアップされている各船舶の運航状況の実績及び確定している予定を表す情報である。積荷から揚荷までを一つの航海として取り扱い、航海Ｎｏが付される。各航海について、積－積－揚、積－揚－揚のように、積港及び揚港は１港の場合も、複数港の場合もある。船舶リストにリストアップされている各船舶について、航海Ｎｏ．、積揚ドック区分、積揚連番、積揚港コード、バースコード、積揚港沖着の日時（ＥＴＡ）、積揚港着岸の日時（ＥＴＢ）、積揚港出港の日時（ＥＴＤ）、航海時間が記載される。 As shown in FIG. 14, the ship operation status is information indicating the actual operation status and the confirmed schedule of each ship listed in the ship list. Handling from loading to unloading is handled as one voyage, and voyage number is attached. For each voyage, the loading and unloading ports may be one port or multiple ports, such as loading-loading-lifting and loading-lifting-lifting. For each ship listed in the ship list, the voyage No. , Loading dock classification, loading serial number, loading port code, berth code, loading and unloading port date and time (ETA), loading and unloading port date and time (ETB), loading and unloading port date and time (ETD), The voyage time is listed.
　例えば連続航海船Ａの航海Ｎｏ．３とは、２００８年３月７日２０時に積港（Ｘ１港）沖に着き、２００８年３月１２日２０時に積港（Ｘ１港）のコード「１」で表されるバースに着岸し、２００８年３月１４日２０時に積港（Ｘ１港）を出港した後、４６９２０分航海して、２００８年４月１６日１０時に揚港（Ｂ港）沖に着き、２００８年４月１６日１３時に揚港（Ｂ港）のコード「１１」で表されるバースに着岸し、２００８年４月１８日１４時に揚港（Ｂ港）を出港する航海である。また、この船は契約区分が連続航海であるため、連続航海船Ａは、積－揚－積－揚の順に連続的に航海、寄港している。つまり連続航海船Ａは、航海Ｎｏ．２の最後の揚港（Ｄ港）を２００８年２月２２日９時に出港した後、２０８２０時間航海して、２００８年３月７日２０時に航海Ｎｏ．３の最初の積港（Ｘ１港）の沖に着いている。 For example, voyage No. of continuous cruise ship A 3 arrived off the port of loading (X1 port) at 20 o'clock on March 7, 2008, and arrived at the berth represented by code “1” of loading port (port X1) at 20 o'clock on March 12, 2008. After leaving the port (X1 port) at 20 o'clock on March 14, 2008, sailed for 46920 minutes and arrived off the port (B port) at 10 o'clock on April 16, 2008. It is a voyage that sometimes berths at the berth represented by the code “11” of the unloading port (Port B) and leaves the unloading port (Port B) at 14:00 on April 18, 2008. In addition, since this ship is contracted for continuous voyage, continuous voyage ship A sails and calls continuously in the order of loading, unloading, loading and unloading. That is, the continuous voyage ship A has voyage No. No. 2 last port (Port D) departed at 9:00 on February 22, 2008, sailed for 20820 hours, and sailed No. 2 on March 7, 2008 at 20:00. It is arriving off the first port of No. 3 (X1 port).
　立案開始日（配船計画を立案する対象期間の始めの日）が、立案を実行する日に対して、将来である場合は、第１の配合計画作成装置１００で作成された配合計画から、原材料の在庫状況が算出される、立案開始日における製鉄所（揚地）別、銘柄別の在庫状況を表す情報である。また、立案開始日が立案を実行する日に対して、過去の場合は、各製鉄所がデータベース４００にインプットした原材料の銘柄別の実績在庫状況を表す情報である。 If the planning start date (the first day of the target period for formulating a ship allocation plan) is in the future with respect to the date of planning, from the formulation plan created by the first formulation plan creation device 100, It is information representing the stock status of each steelworks (land of unloading) and brand on the start date of planning, where the stock status of raw materials is calculated. Further, in the past, when the planning start date is the date when the planning is executed, it is information that represents the actual stock status of each material brand input to the database 400 by each steelworks.
　原材料の購入費用情報は、山元（積地）別、銘柄別の原材料の単価（＄／トン（ｔｏｎ、ｔ））を表す情報である。 The raw material purchase cost information is information indicating the unit price ($ / ton (ton, t)) of the raw material by Yamamoto (loading place) and by brand.
　輸送費用情報は、船舶リストにリストアップされている船舶を利用する場合のフレート、及び、船舶リストにリストアップされている船舶を利用する場合の積揚港別の滞船料を表す情報である。 The transportation cost information is information representing the freight when using a ship listed in the ship list and the berthing fee for each loading port when using a ship listed in the ship list. .
　図１５には、フレートのリストの例を示す。同図に示すように、船舶リストにリストアップされている各船舶について、傭船コード、積港、１揚港、２揚港、３揚港、フレート（＄／トン）が記載されている。例えば連続航海船Ａは、積港Ｘ１から揚港Ａまで航海した場合のフレートが１６．００であり、積港Ｘ１から揚港Ａを経て揚港Ｂまで航海した場合のフレートが１６．２４である。なお、フレートのリストからもわかるように、一般的には、連続航海船を利用した方が不定期船やスポット船を利用するよりもフレートが安い。 FIG. 15 shows an example of a freight list. As shown in the figure, for each ship listed in the ship list, dredger code, loading port, 1 lifting port, 2 lifting port, 3 lifting port, freight ($ / ton) are described. For example, a continuous cruise ship A has a freight of 16.00 when sailing from loading port X1 to unloading port A, and a freight when sailing from loading port X1 to unloading port B via unloading port A is 16.24. is there. As can be seen from the freight list, the freight rate is generally cheaper using a continuous cruise ship than using an irregular ship or a spot ship.
　図１６には、滞船料のリストの例を示す。同図に示すように、船舶リストにリストアップされている各船舶について、傭船コード、揚ラン（ｔ／Ｄａｙ）、デスデマレート（＄／日）が記載されている。揚ラン（Ｄｉｓｃｈａｒｇｉｎｇ　Ｒａｔｅ）とは、契約上の基準となる荷揚能力であり、１日で荷役ができる量を表す。その荷揚能力で荷を揚げると仮定した場合と比較して、実際の揚げ時間が早くなった場合は、デスデマレートで設定された金額を船会社から受け取ることができる。逆に遅くなればデスデマレートに設定された金額を船会社に支払うこととなる。デスデマレート（Ｄｅｓｐａｔｃｈ／Ｄｅｍｕｒａｇｅ　Ｒａｔｅ）とは、積・揚港において、早出（Ｄｅｓｐａｔｃｈ）、滞船（Ｄｅｍｕｒａｇｅ）が発生した場合に行われる、課金または払い戻しについて、契約上の料金レートを併せて指す用語である。本明細書の各式中では、デマレート（Ｄｅｍｕｒａｇｅ　Ｒａｔｅ）の記載も用いる。例えば、連続航海船Ａが１００００トンの荷揚げをした際に、ＥＴＡから１１時間後にＥＴＤした場合を考える。揚ランは２００００（ｔ／Ｄａｙ）であるため、荷揚には１２時間かかる見込みとなる。この場合に１１時間で荷揚げすれば、デスデマレート１６２５０（＄／日）で規定された金額の１時間分＝１６２５０／２４＄を船会社より受け取る。逆に、ＥＴＡから１３時間後にＥＴＤした場合は、デスデマレート１６２５０（＄／日）で規定された金額の１時間分＝１６２５０／２４＄を船会社に支払う。 Fig. 16 shows an example of a list of berthing charges. As shown in the figure, for each ship listed in the ship list, a dredger code, a lift run (t / Day), and a desdemarate ($ / day) are described. Lifting rate is the standard capacity for unloading and represents the amount that can be handled in one day. Compared to the case where it is assumed that the cargo can be lifted with the lifting capacity, when the actual lifting time is shortened, the amount set in the desdemaration rate can be received from the shipping company. On the contrary, if it becomes late, the amount set for the death demarcation will be paid to the shipping company. Death Demarcation (Despatch / Demage Rate) is a term that also refers to the contracted fee rate for charges or refunds that are made in the event of an early departure (Despatch) or a berthing (Demage) at a loading / unloading port. is there. In each formula in this specification, the description of demarcation rate is also used. For example, let us consider a case where ETD 11 hours after ETA when a continuous cruise ship A unloads 10,000 tons. Since the lifting run is 20000 (t / Day), the unloading is expected to take 12 hours. In this case, if the cargo is unloaded in 11 hours, the amount of 1 hour = 16250/24 $ of the amount defined by the desdemarate 16250 ($ / day) is received from the shipping company. On the other hand, if ETD is made 13 hours after ETA, 1 hour = 16250/24 $ of the amount specified by desdemaration rate 16250 ($ / day) is paid to the shipping company.
（２）船舶財源リストの作成（ステップＳ１０２）
　マクロ最適化部２０２の船舶財源リスト作成部２０２ａは、ステップＳ１０１で取り込んだ船舶リスト（図１３を参照）から、配船計画の以降の処理の対象となる、あるいは対象となる可能性のある船舶を選択し、船舶財源リストを作成する。 (2) Creation of ship finance list (step S102)
The ship financial resource list creation unit 202a of the macro optimization unit 202 is a ship that is or may be a target of subsequent processing of the ship allocation plan from the ship list (see FIG. 13) captured in step S101. To create a ship funding list.
　図１７は、船舶の選択処理を説明するためのフローチャートである。船舶財源リスト作成部２０２ａは、まず、船舶リスト（図１３を参照）及び船舶運航状況（図１４を参照）に基づいて、計画作成期間において運航予定の未定部分がある連続航海船を抽出する（ステップＳ２０１）。例えば立案開始日を２００８年３月１日として３ヶ月分の配船計画を作成するとしたならば、図１４に示すように、連続航海船Ａは２００８年４月１８日以降が未定となっているので、連続航海船Ａは抽出される。 FIG. 17 is a flowchart for explaining a ship selection process. The ship funding list creation unit 202a first extracts a continuous sailing ship having an undetermined portion scheduled for operation in the plan creation period based on the ship list (see FIG. 13) and the ship operation status (see FIG. 14) ( Step S201). For example, assuming that the planning start date is March 1, 2008 and a ship allocation plan for three months is to be created, as shown in FIG. 14, the continuous cruise ship A has not been determined since April 18, 2008. Therefore, the continuous cruise ship A is extracted.
　そして、抽出した連続航海船Ａについて計画作成期間における積地と揚地の組み合わせのパターンを全て作成する（ステップＳ２０２）。このとき、積地と揚地との距離等に基づいて特定の条件を設け、この条件を満たすパターンを全て作成しても良い。この場合、例えば明らかに不適な運行距離を持つパターン等を予め排除でき、シミュレーションの効率を上げられる。図１８は、既に確定している航海Ｎｏ．３（図中「Ａ－３」）に続けて、積港Ｘ２から揚港Ａ（航海Ｎｏ．４）、積港Ｘ１から揚港Ｂ（航海Ｎｏ．５）のパターンを作成している様子を示す図である。パターンの作成に際して、各時刻は、標準的な航海時間（港間距離及びこの船舶Ａの標準ノット）や標準的な積揚時間を使用して求めるようにしている。例えば、航海Ｎｏ．４における揚港Ａの沖着時刻は、［航海Ｎｏ．４における積港Ｘ２の沖着時刻］＋［標準積時間］＋［港Ｘ２と港Ａとの距離］／［船舶Ａの標準ノットで求めることができる。もちろん連続航海船Ａについて計画作成期間における積地と揚地の組み合わせは複数あるので、それら全て（あるいは上記の特定条件に合致するパターンを全て）のパターンを作成する。他の連続航海船についても同様の作業を行う。 Then, all patterns of combinations of loading and unloading points in the plan creation period are created for the extracted continuous cruise ship A (step S202). At this time, a specific condition may be provided based on the distance between the loading site and the landing site, and all patterns that satisfy this condition may be created. In this case, for example, a pattern having a clearly inappropriate driving distance can be excluded in advance, and the efficiency of the simulation can be increased. FIG. 18 shows the voyage No. already confirmed. 3 (“A-3” in the figure), the pattern of loading port X2 to unloading port A (voyage No. 4) and loading port X1 to unloading port B (voyage No. 5) is shown. FIG. When creating the pattern, each time is obtained using a standard voyage time (distance between ports and a standard knot of the ship A) and a standard loading time. For example, voyage no. The time of landing at Yacht A in No. 4 is [voyage no. 4 at the time of landing at loading port X2] + [standard loading time] + [distance between port X2 and port A] / [standard knot of vessel A]. Of course, since there are a plurality of combinations of loading and unloading sites in the plan creation period for the continuous cruise ship A, all of these patterns (or all patterns that meet the above specific conditions) are created. The same operation will be performed for the other continuous cruise ships.
　次に、船舶リスト（図１３を参照）及び船舶運航状況（図１４を参照）に基づいて、計画作成期間において利用可能で未定部分がある不定期船を抽出する（ステップＳ２０３）。例えば、図１３に示すように、不定期船５の配船予定年月は計画作成期間から外れているので、不定期船５は抽出されない。そして、連続航海船の場合と同様に、抽出した各不定期船について計画作成期間における積地と揚地の組み合わせのパターンを全て（あるいは特定条件に合致するパターンを全て）作成する（ステップＳ２０４）。 Next, based on the ship list (refer to FIG. 13) and the ship operation status (refer to FIG. 14), an irregular ship that can be used in the plan creation period and has an undetermined portion is extracted (step S203). For example, as shown in FIG. 13, since the scheduled allocation date of the irregular ship 5 is out of the plan creation period, the irregular ship 5 is not extracted. Then, as in the case of a continuous voyage ship, all patterns of combination of loading and unloading sites (or all patterns that meet specific conditions) are created for each extracted irregular ship in the plan creation period (step S204). .
　次に、船舶リスト（図１３を参照）に基づいて、スポット船の候補を抽出する（ステップＳ２０５）。具体的には、まず計画作成期間における総引取目標量を計算する。また、ステップＳ２０１、Ｓ２０２で抽出した連続航海船及び不定期船の最大積載量の合計を計算する。これにより、スポット船で補うべき輸送量を、総引取目標量から、計画作成期間にはいる連続航海船及び不定期船の最大積載量の合計を減算することで、算出することができる（図１９を参照）。このスポット船で補うべき輸送量に基づいて、各スポット船の最大積載量を参照し、何隻のスポット船が必要となるかを計算し、各スポット船の最少船数を求める。例えばスポット船で補うべき輸送量が２５００００トンである場合、豪州-ＰｍａｘＳｐｏｔであれば２５００００÷７５０００＝３．３３で４隻必要となり、４隻の豪州-ＰｍａｘＳｐｏｔを契約するスポット船の候補とする。同様に、２隻の豪州-ＣａｐｅＳｐｏｔ、１隻の豪州-ＶＬＳｐｏｔ、４隻のカナダ-ＰｍａｘＳｐｏｔ、２隻のカナダ-ＣａｐｅＳｐｏｔ、１隻のカナダ-ＶＬＳｐｏｔ、１隻の豪州-ＰｍａｘＳｐｏｔ及び１隻の豪州-ＣａｐｅＳｐｏｔ、・・・のようにスポット船の最少船数が求まる。ここで求めたスポット船の最少船数は、当該傭船コードのスポット船のみで引取を補った場合に必要となる最少のスポット船数となる。後述するように、この最少船数より多くのスポット船が必要になる場合がある。 Next, spot ship candidates are extracted based on the ship list (see FIG. 13) (step S205). Specifically, first, the total take-off target amount in the plan creation period is calculated. Further, the sum of the maximum loading capacity of the continuous voyage ship and the irregular ship extracted in steps S201 and S202 is calculated. As a result, the transport volume to be supplemented by the spot ship can be calculated by subtracting the total of the maximum loading capacity of the continuous voyage ship and the irregular ship in the planning period from the total take-up target quantity (Fig. 19). Based on the transport amount to be supplemented by this spot ship, the maximum load capacity of each spot ship is referred to calculate how many spot ships are required, and the minimum number of each spot ship is obtained. For example, if the transport amount to be supplemented by the spot ship is 250,000 tons, if Australia-PmaxSpot, 450,000 are required as 250,000 ÷ 75000 = 3.33, and four Australia-PmaxSpots are candidates for a spot ship to be contracted. Similarly, 2 Australia-CapeSpot, 1 Australia-VLSpot, 4 Canada-PmaxSpot, 2 Canada-CapeSpot, 1 Canada-VLSpot, 1 Australia-PmaxSpot and 1 Australia- The minimum number of spot ships is obtained as in CapSpot. The minimum number of spot ships obtained here is the minimum number of spot ships required when the take-up is supplemented only with the spot ship of the dredger code. As will be described later, more spot ships may be required than the minimum number of ships.
　次に、船舶リスト（図１３を参照）及び船舶運航状況（図１４を参照）に基づいて、スポット船の候補を抽出する。ここでは、船舶運航状況で確定された予定がある場合には、当該船舶をスポット船の候補として抽出し、更に船舶リストの契約区分が未契約の傭船コードのそれぞれに対して、予め設定した日にち毎に、計画作成期間分のスポット船の候補を作成する。図２０に、各傭船コードに対するスポット船の候補を作成する間隔を１０日としたスポット船の航路リストの例を示す。 Next, spot ship candidates are extracted based on the ship list (see FIG. 13) and the ship operation status (see FIG. 14). Here, if there is a schedule determined by the ship operation status, the ship is extracted as a candidate for a spot ship, and further, the date set in advance for each charter code for which the contract classification of the ship list is not contracted. Each time a candidate for a spot ship is created for the plan creation period. FIG. 20 shows an example of a route list of spot ships with an interval for creating spot ship candidates for each charter code as 10 days.
　ここで、上記等間隔で作成した船数と、上記計算した最少船数を比較して、上記等間隔で作成した船数の方が少ない場合には、全てのスポット船の候補を雇ったとしても、引取目標量を満足する引取量を実現することが難しい場合がある。このため、上記計算した最少船数より船数が多くなるように、スポット船の候補を作成する間隔を狭めて、スポット船の候補を作成する。そして、連続航海船の場合と同様に、作成した各スポット船の候補について、計画作成期間における積地と揚地の組み合わせのパターンを全て（あるいは特定条件に合致する組み合わせのパターンを全て）作成する（ステップＳ２０６）。ここで、後述するマクロ最適化において、上記スポット船の各候補について雇う、雇わないが判断され、必要となる船型（船の最大積載量を基に定義される船舶の大きさ）、船数分のスポット船が決定される。例えば、豪州-ＰｍａｘＳｐｏｔ-航海Ｎｏ．３が、候補として作成された後、マクロ最適化にておいて、雇わないと計画されることもある。 Here, if the number of ships created at the same interval is compared with the calculated minimum number of ships, and the number of ships created at the same interval is smaller, all spot ship candidates are hired. However, it may be difficult to realize a take-up amount that satisfies the take-up target amount. Therefore, the spot ship candidates are created by narrowing the interval for creating spot ship candidates so that the number of ships is larger than the calculated minimum number of ships. Then, as in the case of a continuous voyage ship, create all patterns of combination of loading and unloading sites (or all patterns that match specific conditions) for each candidate spot ship created during the planning period. (Step S206). Here, in the macro optimization described later, it is determined whether to hire or not hire each spot ship candidate, and the required ship type (the size of the ship defined based on the maximum load capacity of the ship), the number of ships The spot ship is determined. For example, Australia-PmaxSpot-voyage No. After 3 is created as a candidate, it may be planned not to hire in macro optimization.
（３）マクロ数式モデルの設定（ステップＳ１０３）
　マクロ最適化部２０２の数式モデル設定部２０２ｂは、ステップＳ１０２で作成した船舶の運航制約、揚地での原材料の需給バランス制約、引取目標量制約を表すよう構築された数式モデルを設定する。設定を受ける数式モデルは、例えばＬＰ（線形計画法）、ＭＩＰ（混合整数計画法）、ＱＰ（２次計画法）等の数理計画法に則ったモデルとして構築（定式化）されている。ここでは、例としてＭＩＰの定式化に基づいた数式モデルを示す。ここで数式モデルの設定とは、船舶数や港数などの変化に対応できるように抽象的な形式で構築されている基礎数式モデルに対して、各配列の添え字の最大数（例えば船舶数を表す）や、式中の係数の値などを、実際の計画に沿って具体的に定めることを言う。 (3) Setting of a macro mathematical model (step S103)
The mathematical model setting unit 202b of the macro optimization unit 202 sets a mathematical model constructed so as to represent the ship operation restriction, the supply and demand balance restriction of raw materials at the landing, and the take-up target quantity restriction created in step S102. The mathematical model to be set is constructed (formulated) as a model according to mathematical programming such as LP (linear programming), MIP (mixed integer programming), QP (quadratic programming), and the like. Here, as an example, a mathematical model based on the MIP formulation is shown. Here, the setting of the mathematical model refers to the maximum number of subscripts in each array (for example, the number of ships) for the basic mathematical model that is constructed in an abstract format so that it can cope with changes in the number of ships, the number of ports, etc. ) And the coefficient values in the formula are specifically determined according to the actual plan.
　まず、当該船が、当該積港を選択する、選択しないかを示す変数を定義する。この変数は選択する場合を示す１、選択しない場合を示す０のいずれかの値を取る整数変数とする。後述する最適化によって得られるこの変数の値に基づいて、当該積港を選択するか、選択しないかが判断される。 First, define a variable that indicates whether the ship selects or does not select the port. This variable is an integer variable that takes one of the values 1 for selecting and 0 for not selecting. Based on the value of this variable obtained by optimization described later, it is determined whether or not to select the loading port.
Figure JPOXMLDOC01-appb-M000018
Figure JPOXMLDOC01-appb-M000018
　例えば、図１４に示す連続航海船Ａが候補となる船として挙げられ、この船の航海Ｎｏ．４（図中「Ａ－４」）において、当該船舶の寄航可能な積港がＸ１、Ｘ２の２つある場合には、各積港に対応するように、以下の２つの整数変数を定義する。ここで、これらの整数変数の第３の添え字であるＥＴＡは、ステップＳ１０２で計算された沖着時刻である。 For example, the continuous cruise ship A shown in FIG. 4 (“A-4” in the figure), if there are two ports X1 and X2 where the ship can call, define the following two integer variables to correspond to each port To do. Here, ETA, which is the third subscript of these integer variables, is the offshore time calculated in step S102.
Figure JPOXMLDOC01-appb-M000019
Figure JPOXMLDOC01-appb-M000019
　もし、最適化の結果としてＸ１に寄航することが選択された場合は、変数は以下の値を取ることとなる。 If it is chosen to stop at X1 as a result of optimization, the variable will take the following values:
Figure JPOXMLDOC01-appb-M000020
Figure JPOXMLDOC01-appb-M000020
　また、当該船が、当該積港、当該揚港、当該寄航順（揚港の何番目に寄ったかを表す数字、例えば積港Ｘ１、揚港Ａ、揚港Ｂの順に寄港した場合、揚港Ｂは寄航順２とする）を選択するか、選択しないか、を示す整数変数を定義する。つまり、当該積港に寄った後、当該揚港に、当該寄航順で寄港することを選択する場合、この変数は１の値を取る。一方、このような積港、揚港、寄航順の組み合わせを選択しない場合、この変数は０の値を取る。ここで扱う例では、最大２揚港まで寄航できる例を提示するが、寄航できる揚港数、寄航できる積港数は、それ以上の値を取っても構わない。 In addition, if the ship calls in the order of the loading port, the unloading port, the calling order (number indicating the number of the unloading port, for example, loading port X1, unloading port A, unloading port B, Port B is defined as an integer variable indicating whether or not to select (call order 2). In other words, this variable takes a value of 1 when selecting to call at the port of discharge in the order of calling after the port of arrival. On the other hand, if such a combination of loading port, landing port and calling order is not selected, this variable takes a value of zero. In the example dealt with here, an example in which a maximum of two landing ports can be visited is presented. However, the number of landing ports that can be visited and the number of loading ports that can be called may take on more values.
Figure JPOXMLDOC01-appb-M000021
Figure JPOXMLDOC01-appb-M000021
　また、当該船が、当該積港で、当該銘柄を荷積する量を示す変数を定義する。 Also, a variable that indicates the amount that the ship will load the brand at the loading port is defined.
Figure JPOXMLDOC01-appb-M000022
Figure JPOXMLDOC01-appb-M000022
　また、当該船が、当該積港、当該揚港、当該寄航順で、当該銘柄を荷揚げする量を示す変数を定義する。 Also, a variable indicating the amount that the ship unloads the brand in the loading port, the landing port, and the calling order is defined.
Figure JPOXMLDOC01-appb-M000023
Figure JPOXMLDOC01-appb-M000023
　更に、当該日、当該銘柄の、当該揚港での在庫量を示す変数を定義する。 Furthermore, a variable indicating the stock quantity of the brand at that port is defined on that day.
Figure JPOXMLDOC01-appb-M000024
Figure JPOXMLDOC01-appb-M000024
　　「各船舶の積載量が最大積載量を超えないこと」、「積載量は全部荷揚げすること」、等の条件を示す制約式は、基礎となる数式モデルとして予め構築しておく。後述するように、最適化（ステップＳ１０３～Ｓ１０６）及びシミュレーション（Ｓ１０７）を含む一連の工程は、複数ループ反復して実行できる。初回ループの最適化では、ステップＳ１０１で取り込んだデータに基づいて船舶の運航制約を数式モデルに設定する。第２次ループ（ステップＳ１０３～Ｓ１０７）以降の最適化ではシミュレータ２０１が前回のループで行ったシミュレーション結果を反映させて、数式モデルを設定する。ここでは、各船舶の積載量が最大積載量を超えないこと、積載量は全部荷揚げすること、等を数式モデルとして設定する。 Constraint conditions indicating conditions such as “the load capacity of each ship does not exceed the maximum load capacity” and “the load capacity must be completely unloaded” are constructed in advance as a basic mathematical model. As will be described later, a series of steps including optimization (steps S103 to S106) and simulation (S107) can be executed by repeating a plurality of loops. In the optimization of the initial loop, the ship operation restrictions are set in the mathematical model based on the data captured in step S101. In optimization after the secondary loop (steps S103 to S107), the simulator 201 sets a mathematical model reflecting the simulation result performed in the previous loop. In this case, the mathematical model is set such that the load capacity of each ship does not exceed the maximum load capacity, the entire load capacity is unloaded, and the like.
　各船舶の積載量が最大積載量を超えないという制約は、下記の制約式（式２１）と表される。 The constraint that the load capacity of each ship does not exceed the maximum load capacity is expressed by the following constraint expression (Formula 21).
Figure JPOXMLDOC01-appb-M000025
Figure JPOXMLDOC01-appb-M000025
　積載量は全部荷揚げするという制約は、下記の制約式（式２２）と表される。
Figure JPOXMLDOC01-appb-M000026
 The constraint that the entire loading capacity is unloaded is expressed by the following constraint equation (Formula 22).
Figure JPOXMLDOC01-appb-M000026
　また、揚地での原材料の需給バランス制約としては、図２１に示すような、「各銘柄の在庫量が常に安全在庫量以上確保されている」という制約条件が、数式モデルとして構築されている。初回ループの最適化では、ステップＳ１０１で取り込んだデータに基づいて、更に次ループ（ステップＳ１０３～Ｓ１０７）以降はシミュレータ２０１でのシミュレーション結果を反映させて、この数式モデルが設定される。 In addition, as a constraint on the supply and demand balance of raw materials at the landing site, a constraint condition that “the stock amount of each brand is always secured more than the safety stock amount” as shown in FIG. 21 is constructed as a mathematical model. . In the optimization of the initial loop, this mathematical model is set based on the data fetched in step S101 and reflecting the simulation result in the simulator 201 in the subsequent loop (steps S103 to S107) and thereafter.
　まず、各銘柄の在庫量の推移を表す制約式は、下記の（式２３）と表される。つまり、当日の在庫量から前日の在庫量と当日に荷揚する量を引いた値は、当日の使用予定量となる。 First, the constraint equation representing the transition of the stock quantity of each brand is expressed as (Equation 23) below. That is, a value obtained by subtracting the inventory amount on the previous day and the amount unloaded on the current day from the inventory amount on the current day is the scheduled use amount on the current day.
Figure JPOXMLDOC01-appb-M000027
Figure JPOXMLDOC01-appb-M000027
　各銘柄の在庫量が常に安全在庫以上確保されているという制約は、下記の制約式（式２４）と表される。 The constraint that the stock quantity of each brand is always secured above the safety stock is expressed by the following constraint equation (Formula 24).
Figure JPOXMLDOC01-appb-M000028
Figure JPOXMLDOC01-appb-M000028
　また、揚港で荷揚げされた原材料は、ヤードに積上げられるが、この荷揚げされる原材料の在庫量は、ヤード能力の上限以下になっていないと着岸できない。但し、ヤードに積上げられた原材料は、日にちが経過すれば、つまり船舶を待たせれば荷揚げすることができる。しかし、荷揚げ量がヤード能力を一定量以上超えていると、滞船時間が無視できないほど大きくなる。そこで、例えば「荷揚げ量について、ヤード能力の１％程度の超過まで許容する」という制約を定式化する。この制約は、下記の制約式（式２５）と表される。 In addition, the raw materials unloaded at the unloading port are piled up in the yard, but the stock of the unloaded raw materials cannot be docked unless the yard capacity is below the upper limit. However, the raw material stacked in the yard can be unloaded if the date has passed, that is, if the ship is kept waiting. However, if the amount of unloading exceeds a certain amount of yard capacity, the berthing time becomes so large that it cannot be ignored. Therefore, for example, a constraint that “the amount of unloading is allowed to exceed about 1% of the yard capacity” is formulated. This constraint is expressed as the following constraint equation (Equation 25).
Figure JPOXMLDOC01-appb-M000029
Figure JPOXMLDOC01-appb-M000029
　また、引取目標量制約について、初回ループでは、ステップＳ１０１で取り込んだデータに基づいて、更に次ループ（ステップＳ１０３～Ｓ１０７）以降はシミュレータ２０１でのシミュレーション結果を反映させて、設定が行われる。引取目標量制約に関して、例えば、最適化する引取量（荷積量）が引取目標量から一定幅以上離れないこと、引取の可否（前述したように所定の銘柄については所定の期間は引取しないといった事情もありうる）、等が数式モデルに構築されている。ここで、引取量が引取目標量から一定幅以上離れないという制約を考える場合に、例えば図２２Ａに示すように、単に旬毎（或いは月毎）に引取目標量に対して上下限値を設定し、荷積量がその上下限値を超えないことを制約とすることが考えられる。しかしながら、その場合、例えば荷積量が下限値を満たしているが引取目標量を下回る状況が続いたような場合、年間で蓄積すると、引取割れが発生してしまうこともありうる。そこで、図２２Ｂに示すように、旬毎（或いは月毎）にそれまで引取目標量累積及び引取量累積を考え、引取目標量累積と引取量累積との差を小さくする（最小とする、上下限値を越えないようにする等）制約を設定するのが好適である。上記制約式を定式化するために、旬毎の引取目標累積量からの溢れ量、不足量を示す変数を定義する。 Also, the take-up target amount restriction is set in the initial loop by reflecting the simulation result in the simulator 201 after the next loop (steps S103 to S107) after the data fetched in step S101. Regarding takeover target amount constraints, for example, the takeover amount (loading amount) to be optimized should not deviate from the takeover target amount by more than a certain width, whether or not it can be picked up (as mentioned above, for a given brand, it will not be taken out for a given period Etc.) are built into the mathematical model. Here, when considering the restriction that the pick-up amount does not deviate from the pick-up target amount by a certain width or more, for example, as shown in FIG. 22A, upper and lower limit values are simply set for the pick-up target amount every season (or every month) However, it is conceivable that the loading amount does not exceed the upper and lower limit values. However, in that case, for example, when the load amount satisfies the lower limit value but continues to be lower than the take-up target amount, the take-up crack may occur if accumulated for the year. Therefore, as shown in FIG. 22B, taking into account the collection target amount accumulation and the collection amount accumulation every season (or every month), the difference between the collection target amount accumulation and the collection amount accumulation is reduced (minimized, up It is preferable to set a constraint such that the lower limit value is not exceeded. In order to formulate the above constraint equation, a variable indicating an overflow amount and a deficiency amount from the seasonal collection target cumulative amount is defined.
Figure JPOXMLDOC01-appb-M000030
Figure JPOXMLDOC01-appb-M000030
　また、各旬の引取量累積を示す変数を定義する。 Also define a variable that indicates the cumulative amount of each season.
Figure JPOXMLDOC01-appb-M000031
Figure JPOXMLDOC01-appb-M000031
　まず、各銘柄の引取量累積を表す制約式は下記の（式２６）と表される。つまり、引取量累積は、立案開始日から当該旬までの期間にＥＴＡが入っている船舶（航海）の荷揚量の合計となる。 First, the constraint equation representing the accumulated amount of each brand is expressed as (Equation 26) below. That is, the collected amount is the sum of the unloading amount of the ship (voyage) in which the ETA is entered during the period from the planning start date to the season.
Figure JPOXMLDOC01-appb-M000032
Figure JPOXMLDOC01-appb-M000032
　各銘柄の引取目標量累積と溢れ量、不足量との関係を表す制約式は下記の（式２７）と表される。つまり、引取累積量から、溢れが生じている場合は溢れ量を引き、不足が生じている場合は不足量を足すと、引取目標累積量と一致する。ここで、引取累積量と引取目標累積量は近い量を取る程良い計画であるといえる。つまり、この溢れ量、及び不足量は少ない程良い。上記理由のため、後述するようにこの溢れ量、及び不足量は、目的関数の項目として追加され、最適化によってミニマム化される。 制約 The constraint equation that expresses the relationship between the accumulation of the collection target amount of each brand, the overflow amount, and the shortage amount is expressed as (Equation 27) below. That is, if the overflow amount is subtracted from the take-up accumulated amount, and if the deficiency is added, the deficit amount is added. Here, it can be said that the closer the take-up cumulative amount and the take-up target cumulative amount are, the better the plan. That is, the smaller the overflow amount and the insufficient amount, the better. For the above reason, as described later, the overflow amount and the shortage amount are added as items of the objective function, and are minimized by optimization.
Figure JPOXMLDOC01-appb-M000033
Figure JPOXMLDOC01-appb-M000033
　ここで、目的関数としてフレートの合計金額のミニマム化を定式化するために、寄港順を示す整数変数を導入する。この寄港順変数は、特定の船舶が、積港として特定の積港、第一揚港として当該揚港１、第二揚港として特定の揚港２の組み合わせを選択する場合を示す１、この組み合わせの寄港順を選択しない場合を示す０、のいずれかの値を取る。 Here, in order to formulate the minimum of the total amount of freight as an objective function, an integer variable indicating the order of port calls is introduced. This port-calling variable is 1, which indicates a case where a specific ship selects a combination of a specific loading port as a loading port, a corresponding unloading port 1 as a first unloading port, and a specific unloading port 2 as a second unloading port. It takes one of the values 0, which indicates the case where the combination port order is not selected.
Figure JPOXMLDOC01-appb-M000034
Figure JPOXMLDOC01-appb-M000034
　この論理関係を混合整数計画法の定式として記述する方法が、一般的に良く知られており、以下のように定式化することができる。 The method of describing this logical relationship as a mixed integer programming formula is generally well known and can be formulated as follows.
Figure JPOXMLDOC01-appb-M000035
Figure JPOXMLDOC01-appb-M000035
（４）マクロ数式モデル及び目的関数に基づいて最適化（ステップＳ１０４）
　マクロ最適化部２０２の最適化計算部２０２ｃは、ステップＳ１０３で設定した数式モデルを用いて、輸送費用に関して構築された目的関数（評価関数）に基づいて最適化計算を行う。最適化計算に際しては、例えばＬＰ（線形計画法）、ＭＩＰ（混合整数計画法）、ＱＰ（２次計画法）等の数理計画法により最適化問題として問題を解く。 (4) Optimization based on the macro mathematical model and the objective function (step S104)
The optimization calculation unit 202c of the macro optimization unit 202 performs optimization calculation based on the objective function (evaluation function) constructed with respect to the transportation cost, using the mathematical model set in step S103. In the optimization calculation, the problem is solved as an optimization problem by mathematical programming such as LP (Linear Programming), MIP (Mixed Integer Programming), and QP (Secondary Programming).
　ここでの最適化計算では、輸送費用のうちフレートの合計金額のミニマム化を目的とした目的関数を用い、下記の変数の値が決定される。これにより、フレートの合計金額を最も安価にする船型（船の最大積載量を基に定義される船舶の大きさ）、船数、積揚地（積揚港）、積揚銘柄、積揚量が選定される。 In the optimization calculation here, the following variable values are determined using an objective function for the purpose of minimizing the total amount of freight in the transportation cost. As a result, the hull type (the size of the ship defined based on the maximum loading capacity of the ship), the number of ships, the landing site (shipping port), the loading brand, the loading quantity that makes the total amount of freight the cheapest Is selected.
Figure JPOXMLDOC01-appb-M000036
Figure JPOXMLDOC01-appb-M000036
　ここで、船舶に掛かるフレートは、積港から第一港目に寄航する揚港までの基準フレートと、上記に更に他の港に寄航した際に掛かる多港揚追加フレートの合計、ここでは、第一揚港から第二揚港に余分に寄航した際に発生する多港揚追加フレートの合計と、積載した量との積となる。 Here, the freight applied to the ship is the sum of the standard freight from the loading port to the landing port calling at the first port and the multi-port additional freight applied when calling to another port as described above. Then, it is the product of the total of the multi-port lift additional freight that occurs when an extra call is made from the first port to the second port, and the amount loaded.
　例えば、図１５より連続航海船Ａ－航海Ｎｏ．１がＸ１港から第一揚港Ａに７５０００トンの荷を運んだ際には、基準フレート１６．００となり、フレート（雇船費用）は１６．００×７５０００＝１,２００,０００となる。また、第二揚港としてＡに寄った後、Ｂによると多港揚追加フレートは（１６．２４－１６．００）＝０．２４となり、この際の雇船費用は（１６．００＋０．２４）×７５０００＝１,２１８,０００となる。 For example, from FIG. When 1 carries 75,000 tons of cargo from the X1 port to the first landing port A, the standard freight is 16.00, and the freight (shipment cost) is 16.00 × 75000 = 1,200,000. In addition, after stopping at A as the second port, according to B, the multi-port lift additional freight was (16.24-16.00) = 0.24, and the hiring cost at this time was (16.00 + 0.24). ) × 75000 = 1,218,000.
　以上より、マクロ最適化で用いる目的関数（以下マクロ目的関数と呼ぶ）を式で表すと、下記の式（式３１）を得る。 From the above, when the objective function used in macro optimization (hereinafter referred to as macro objective function) is expressed by an equation, the following equation (Equation 31) is obtained.
Figure JPOXMLDOC01-appb-M000037
Figure JPOXMLDOC01-appb-M000037
　ここで、マクロ最適化では、引取目標量累積及び引取量累積を考え、引取目標量累積と引取量累積との差を小さくすることも目的としている。このため、旬毎の引取目標累積量からの溢れ量、不足量の合計量をミニマム化する項目を目的関数に追加する。このため、目的関数を表す（式３１）を下記の式（式３２）に変更する。
　マクロ最適化では、全体として傭船に関する問題を最適化する。 Here, the macro optimization is intended to reduce the difference between the take-up target amount accumulation and the take-up amount accumulation in consideration of the take-up target amount accumulation and the take-up amount accumulation. For this reason, an item for minimizing the total amount of overflow and deficiency from the seasonal collection target cumulative amount is added to the objective function. Therefore, (Equation 31) representing the objective function is changed to the following equation (Equation 32).
Macro optimization optimizes problems related to dredgers as a whole.
Figure JPOXMLDOC01-appb-M000038
Figure JPOXMLDOC01-appb-M000038
　なお、フレートに関して目的関数を構築することを説明したが、フレートの合計金額及び原材料の購入費用の合計金額のミニマム化を目的とした目的関数としてもよい。既述したように原材料の引取目標量は契約により定められており、原材料の購入費用に大幅な変動はないが、その中でも原材料の購入費用の合計金額のミニマム化が可能になる。 Although it has been explained that the objective function is constructed for freight, the objective function may be used for the purpose of minimizing the total amount of freight and the total amount of raw material purchase costs. As described above, the target amount of raw materials is set by contract, and there is no significant change in the purchase cost of raw materials, but among them, the total purchase cost of raw materials can be minimized.
　上記の項目（３）、（４）で説明した如く、ミニマム化すべき式が目的関数、満足すべき各式が制約式として定式化されている。上記の制約式は線形等式、或いは不等式で表現されており、上記の目的関数は１次式で表されている。また、変数の中に整数となるべき変数が存在するモデルとして数式モデル、目的関数が構築されている。このように定式化された問題は、混合整数計画問題として一般に良く知られており、本問題は（解析的に）最適化することが可能である。 As explained in the above items (3) and (4), the formula to be minimized is formulated as an objective function, and each formula to be satisfied is formulated as a constraint formula. The constraint equation is expressed by a linear equation or an inequality, and the objective function is expressed by a linear equation. In addition, a mathematical model and an objective function are constructed as models in which there are variables that should be integers. The problem formulated in this way is generally well known as a mixed integer programming problem, and this problem can be (analytical) optimized.
　マクロ最適化に際しては、時間精度を旬精度として演算する。最適化期間は、最初のループ（ステップＳ１０３～Ｓ１０７）では９旬、次ループ（ステップＳ１０３～Ｓ１０７）では８旬、・・・、最後のループ（ステップＳ１０３～Ｓ１０７）では１旬とする。時間精度は、旬精度として演算が行われる。そして、最適化期間（９旬～１旬）のうちの最初の１旬を計画確定期間とし、その計画確定期間での演算結果をミクロ最適化部２０３に出力する。 * In macro optimization, time accuracy is calculated as seasonal accuracy. The optimization period is 9 in the first loop (steps S103 to S107), 8 in the next loop (steps S103 to S107), and so on in the last loop (steps S103 to S107). The time accuracy is calculated as seasonal accuracy. Then, the first January of the optimization period (9th to January) is set as the plan finalization period, and the calculation result in the plan finalization period is output to the micro optimization unit 203.
（５）ミクロ数式モデルの設定（ステップＳ１０５）
　ミクロ最適化部２０３の数式モデル設定部２０３ａは、マクロ最適化部２０２で求めた計画確定期間の配船計画における船舶の運航制約のうち、滞船制約、及び、揚地での原材料の需給バランス制約を表す数式モデルを設定する。ここで用いられる数式モデルは、例えばＬＰ（線形計画法）、ＭＩＰ（混合整数計画法）、ＱＰ（２次計画法）等の数理計画法に則って構築されている。ここでは、例としてＭＩＰの定式化に基づいた数式モデルを示す。 (5) Setting of a micro mathematical model (step S105)
The mathematical model setting unit 203a of the micro-optimization unit 203 is a ship operation constraint in the ship allocation plan in the plan determination period obtained by the macro optimization unit 202, and a balance between supply and demand of raw materials at the landing site. Set the mathematical model that represents the constraint. The mathematical model used here is constructed in accordance with a mathematical programming method such as LP (linear programming), MIP (mixed integer programming), QP (quadratic programming), or the like. Here, as an example, a mathematical model based on the MIP formulation is shown.
　マクロ最適化により、寄航する揚港が決定されている。ここで、揚港には船が着岸するために複数のバース（岸壁）が存在するため、当該揚港のいずれのバースに着岸するかを選択する変数を定義する。この変数は当該バースを選択する場合を示す１、選択しない場合を示す０のいずれかの値を取る整数変数とする。 揚 The port of call is determined by macro optimization. Here, since there are a plurality of berths (quay walls) for the ship to berth at the port, a variable for selecting which berth of the port will be defined. This variable is an integer variable that takes one value of 1 indicating that the berth is selected and 0 indicating that the berth is not selected.
Figure JPOXMLDOC01-appb-M000039
Figure JPOXMLDOC01-appb-M000039
　また、当該船が当該バースに着岸するために沖待を開始する時刻（ＥＴＡ）を示す変数を定義する。時刻はＭＩＰで定式化するための変数として直接定義できないため、立案開始日からの経過分として定義する。つまり、立案開始日が１月１日０時０分の場合で、ＥＴＡが１月１日１時１０分の場合は、７０という値を取るとして定義する。また、この変数は整数変数ではなく、連続値を取る変数として定義する。 Also, define a variable that indicates the time (ETA) at which the ship will start offshore to arrive at the berth. Since the time cannot be directly defined as a variable for formulation by MIP, it is defined as the elapsed time from the planning start date. That is, when the planning start date is 0:00 on January 1, and the ETA is 1:10 on January 1, it is defined as taking 70. Also, this variable is not an integer variable, but defined as a variable that takes a continuous value.
Figure JPOXMLDOC01-appb-M000040
Figure JPOXMLDOC01-appb-M000040
　同様にＥＴＢを示す変数を定義する。 Similarly, define a variable indicating ETB.
Figure JPOXMLDOC01-appb-M000041
Figure JPOXMLDOC01-appb-M000041
　同様にＥＴＤを示す変数を定義する。 Similarly, define a variable indicating ETD.
Figure JPOXMLDOC01-appb-M000042
Figure JPOXMLDOC01-appb-M000042
　更に、当該分、当該銘柄の、当該揚港での在庫量を示す変数を定義する。 Furthermore, a variable that indicates the stock quantity of the brand at the port of discharge is defined.
Figure JPOXMLDOC01-appb-M000043
Figure JPOXMLDOC01-appb-M000043
　船舶の滞船制約についても、初回ループではステップＳ１０１で取り込んだデータに基づいて、更に次ループ（ステップＳ１０３～Ｓ１０７）以降はシミュレータ２０１でのシミュレーション結果を反映させて設定する。船舶の滞船制約に関しては、揚港での船舶運行条件（ＥＴＢ＞ＥＴＡ、ＥＴＤ＞ＥＴＢ＋荷揚時間等）、バースの条件（許容されるＬＯＡ（全長）、ＤＲＡＦＴ（全深）、ＢＥＡＭ（全幅）、積揚能力、ヤード能力等）、等の条件が数式モデルとして構築されている。ここでは、これらの数式モデルが設定される。 Vessel stagnation restrictions are also set based on the data fetched in step S101 in the first loop, and the simulation results in the simulator 201 are reflected after the next loop (steps S103 to S107). Regarding ship stagnation restrictions, ship operating conditions (ETB> ETA, ETD> ETB + unloading time, etc.), berth conditions (allowable LOA (full length), DRAFT (full depth), BEAM (full width) , Loading ability, yard ability, etc.) are constructed as a mathematical model. Here, these mathematical models are set.
　マクロ最適化で揚港が決定された船舶は、当該揚港の何れかのバースに着岸する必要がある。このため、この制約は、下記の制約式（式３３）と表される。つまり当該船に対して、着岸可能なバースの内で、必ず一つのバースが選択（変数の値が１）される必要がある。 船舶 Vessels that have been determined to be unloaded by macro optimization need to berth at one of the berths at that port. Therefore, this constraint is expressed as the following constraint equation (Formula 33). In other words, one berth must be selected (variable value is 1) among the berths that can be docked for the ship.
Figure JPOXMLDOC01-appb-M000044
Figure JPOXMLDOC01-appb-M000044
　ＥＴＢはＥＴＡ以降となる必要がある。この制約は、下記の制約式（式３４）と表される。 ETB needs to be after ETA. This restriction is expressed as the following restriction expression (Expression 34).
Figure JPOXMLDOC01-appb-M000045
Figure JPOXMLDOC01-appb-M000045
　ＥＴＤはＥＴＢ＋荷揚時間以降となる必要がある。ここで、マクロ最適化により当該バースでの荷揚量は決定されているため、当該バースでの荷揚時間は、当該バースでの標準的な荷揚能力を用いると、荷揚時間＝荷揚量／荷揚能力となる。以上より、上記制約は、下記の制約式（式３５）と表される。 ETD needs to be after ETB + unloading time. Here, since the amount of unloading at the berth is determined by macro optimization, the unloading time at the berth can be expressed as unloading time = unloading amount / unloading capacity using the standard unloading capacity at the berth. Become. From the above, the above constraint is expressed as the following constraint equation (Formula 35).
Figure JPOXMLDOC01-appb-M000046
Figure JPOXMLDOC01-appb-M000046
　また、上記マクロ最適化と類似の、揚地での原材料の需給バランス制約が、ミクロ最適化（ステップＳ１０５～Ｓ１０７）でも用いられる。この需給バランス制約条件も、初回ループのミクロ最適化ではステップＳ１０１で取り込んだデータに基づいて、次ループ以降のミクロ最適化ではシミュレータ２０１でのシミュレーション結果を反映させて、設定される。ミクロ最適化でも、図２１に示すような、「各銘柄の在庫量が常に安全在庫量以上確保されている」という制約条件を表すように構築された数式モデルが設定される。ただし、ミクロ最適化では、用いられる時間精度がマクロ最適化と異なる。このため、ここで用いられる制約条件は、「当該時刻の在庫量から１分前の在庫量と当該時刻に荷揚する量を引いた値が、当該時刻１分間の使用予定量である」となる。 Also, the supply-demand balance constraint of raw materials at the landing site, similar to the above macro optimization, is also used in micro optimization (steps S105 to S107). This supply and demand balance constraint condition is also set based on the data fetched in step S101 in the micro optimization of the first loop, and reflecting the simulation result in the simulator 201 in the micro optimization after the next loop. Even in the micro-optimization, a mathematical model constructed so as to express the constraint condition that “the stock amount of each brand is always secured at least the safe stock amount” as shown in FIG. 21 is set. However, in micro optimization, the time accuracy used is different from macro optimization. For this reason, the constraint condition used here is “the value obtained by subtracting the inventory amount one minute before the inventory amount at the time and the amount unloaded at the time is the scheduled use amount for the minute at the time”. .
Figure JPOXMLDOC01-appb-M000047
Figure JPOXMLDOC01-appb-M000047
　また、揚港で荷揚げされた原材料は、ヤードに積上げられるが、この荷揚げされた原材料の在庫量は、ヤード能力の上限以下になっていないと着岸できない。つまりＥＴＢ時点での在庫量はヤード能力上限以下となる必要がある。この制約は、下記の制約式（式３７）と表される。 In addition, the raw materials unloaded at the unloading port are piled up in the yard, but the stock of the unloaded raw materials cannot be docked unless the yard capacity is below the upper limit. In other words, the inventory amount at the time of ETB needs to be below the upper limit of yard capacity. This restriction is expressed as the following restriction expression (Expression 37).
Figure JPOXMLDOC01-appb-M000048
Figure JPOXMLDOC01-appb-M000048
（６）ミクロ数式モデル及び目的関数に基づいて最適化（ステップＳ１０６）
　ミクロ最適化部２０３の最適化計算部２０３ｂは、ステップＳ１０５で設定した数式モデルを用いて、輸送費用に関して構築された目的関数（評価関数）に基づいて最適化計算を行う。最適化計算に際しては、例えばＬＰ（線形計画法）、ＭＩＰ（混合整数計画法）、ＱＰ（２次計画法）等の数理計画法により最適化問題として問題を解く。 (6) Optimization based on the micro mathematical model and the objective function (step S106)
The optimization calculation unit 203b of the micro optimization unit 203 uses the mathematical model set in step S105 to perform optimization calculation based on the objective function (evaluation function) constructed for the transportation cost. In the optimization calculation, the problem is solved as an optimization problem by mathematical programming such as LP (Linear Programming), MIP (Mixed Integer Programming), and QP (Secondary Programming).
　ここでの最適化計算では、滞船料の合計金額のミニマム化を目的とした目的関数を用い、船舶が着岸する／しないを表すδ（船舶、バース）、ＥＴＡ時刻を表すＥＴＡ（船舶、バース）、ＥＴＢ時刻を表すＥＴＢ（船舶、バース）、ＥＴＤ時刻を表すＥＴＤ（船舶、バース）といった変数を決定する。これにより、輸送費用を最も安価にするバース、入出港タイミングが選定される。 In this optimization calculation, an objective function for minimizing the total amount of berthing charges is used, δ (ship, berth) indicating whether or not the ship shores, ETA (ship, berth) indicating ETA time. ), ETB representing the ETB time (ship, berth), and ETD representing the ETD time (ship, berth). As a result, the berth and entry / exit timing for the lowest transportation cost is selected.
　ここで、船舶に掛かる滞船料は、ＥＴＤ－ＥＴＡと契約上の基準停泊時間とを比較し、基準停泊時間より、停泊が長い、つまり、ＥＴＤ－ＥＴＡ＞基準停泊時間の場合には、デスデマレートとして契約された費用を支払い、逆の場合には、デスデマレートとして契約された費用を受取ることとなる。基準停泊時間は、契約上設定された荷揚能力である揚ランを用いて揚量／揚ランで計算される。例えば、連続航海船Ａ－航海Ｎｏ．１が揚港で１００００トンの荷揚げを行い、ＥＴＡからＥＴＤまで１１時間掛かった場合は、基準停泊時間＝１００００／２００００＝０．５日、１２時時間より１時間早いため、デスデマレートで設定された１６２５０／２４の金額を船会社より受取ることとなる。以上より、ミクロ最適化で用いる目的関数（以下ミクロ目的関数と呼ぶ）を式で表すと、下記の式（式３８）を得る。 Here, the berthing charge on the ship is compared with the ETD-ETA and the contracted standard berth time, and if the berth is longer than the standard berth time, that is, if ETD-ETA> the standard berth time, the desdemaration rate In the opposite case, the contracted cost will be received as a de-demare rate. The standard berthing time is calculated by the lift / lift run using the lift run, which is the unloading capacity set in the contract. For example, continuous cruise ship A-voyage No. 1 was unloading 10,000 tons at the unloading port, and if it took 11 hours from ETA to ETD, the standard berth time = 10000/20000 = 0.5 days, 1 hour earlier than 12:00 hours. The amount of 16250/24 will be received from the shipping company. From the above, when the objective function used in the micro optimization (hereinafter referred to as the micro objective function) is expressed by an equation, the following equation (Equation 38) is obtained.
Figure JPOXMLDOC01-appb-M000049
Figure JPOXMLDOC01-appb-M000049
　上記式では定数部分が含まれているが、ミニマム化において定数部分は影響を与えないため、下記の式（式３９）が目的関数となる。 Although the constant part is included in the above formula, the constant part has no effect on the minimization, and the following formula (Formula 39) is the objective function.
Figure JPOXMLDOC01-appb-M000050
Figure JPOXMLDOC01-appb-M000050
　上記の項目（５）、（６）で説明した如く、ミニマム化すべき式が目的関数、満足すべき各式が制約式として定式化されている。上記の制約式は、線形等式、或いは不等式で表現され、上記の目的関数は１次式で表されている。また、変数の中に整数となるべき変数が存在するモデルとして数式モデル、目的関数が構築されている。このように定式化された問題は、混合整数計画問題として一般に良く知られており、本問題は（解析的に）最適化することが可能である。 As explained in the above items (5) and (6), the formula to be minimized is formulated as an objective function, and each formula to be satisfied is formulated as a constraint formula. The constraint equation is expressed by a linear equation or an inequality, and the objective function is expressed by a linear equation. In addition, a mathematical model and an objective function are constructed as models in which there are variables that should be integers. The problem formulated in this way is generally well known as a mixed integer programming problem, and this problem can be (analytical) optimized.
　ミクロ最適化に際しては、最適化期間を１０日（１旬）とし、時間精度を分精度として演算する。 In the micro optimization, the optimization period is 10 days (1st) and the time accuracy is calculated as minute accuracy.
（７）シミュレーション（ステップＳ１０７）
　シミュレータ２０１は、ミクロ最適化部２０３で求めた数式モデルに対する解に基づいてシミュレーションを実行して、計画確定期間（１旬）の配船計画を確定する。シミュレーションの時間精度は分精度とする。このシミュレーションでは、マクロ数式モデル、ミクロ数式モデルには組み込むことができなかった制約等も組み込むことで、実際に求められる細かな制約までも考慮した配船計画を作成することが可能となる。 (7) Simulation (step S107)
The simulator 201 executes a simulation based on the solution to the mathematical model obtained by the micro optimization unit 203, and determines the ship allocation plan for the plan determination period (in the first season). The time accuracy of the simulation is minute accuracy. In this simulation, it is possible to create a ship allocation plan that takes into account even the fine constraints actually required by incorporating constraints that could not be incorporated into the macro mathematical model and the micro mathematical model.
　例えば、マクロ・ミクロ最適化で取扱うことが難しい制約の一例として１隻の船舶の荷揚げに使用するアンローダの基数がある。この基数は、荷揚げする銘柄が積載されているハッチの位置、同一揚港の別バースで荷役している船舶があるか、ないか等により変わって来る。この荷揚げに使用するアンローダの基数により荷揚能力は変わって来る。例えば、「１基で荷揚げする場合は、１５００ｔ／ｈで１００％能力で揚げられる」、または、「２基の場合は、１５００ｔ／ｈ×２基で７０％能力で揚げられる」等の状況が例示できる。マクロ・ミクロ最適化ではこれらのアンローダ基数まで考慮されていないため、最適化で計算された時間を、シミュレータによりアンローダ基数まで考慮して、最適化の時間のずれ等をシミュレーションに取込み、正確にシミュレートすることで、実操業に求められる細かな制約まで考慮した生産・物流計画の立案が可能となる。 For example, one example of constraints that are difficult to handle with macro / micro optimization is the number of unloaders used to unload a single ship. This radix varies depending on the position of the hatch where the brand to be unloaded is loaded, whether or not there is a ship handling at another berth at the same port. The unloading capacity varies depending on the number of unloaders used for unloading. For example, “When unloading with one unit, it is fried at 100% capacity at 1500 t / h”, or “When it is two units, it is fried at 1500 t / h × 2 units with 70% capacity” It can be illustrated. In macro / micro optimization, these unloader radixes are not taken into consideration, so the time calculated by optimization is taken into consideration by the simulator up to the unloader radix, and the time lag of the optimization is taken into the simulation and accurately simulated. This makes it possible to create production and logistics plans that take into account the fine constraints required for actual operations.
　シミュレータ２０１では、ミクロ最適化部２０３で船舶の入出港タイミングの入れ替え等による時間調整があった場合、それを波及的に反映させて時刻修正する。特に連続航海船では、ある港で時間調整があった場合その後の航海にも波及的に影響するので、シミュレータ２０１で時刻修正を行い、その後のマクロ最適化部２０２での処理に反映させるようにしている。 In the simulator 201, if the micro optimization unit 203 adjusts the time by changing the timing of entering / leaving the ship, the time is corrected by reflecting it in a spillover manner. In particular, in the case of a continuous voyage ship, if time adjustment is made at a certain port, it will affect the subsequent voyage, so the time will be corrected by the simulator 201 and reflected in the subsequent processing by the macro optimization unit 202. ing.
（８）立案開始日の更新（ステップＳ１０９）
　ステップＳ１０８において計画作成期間（３ヶ月（９旬））分の計画が確定したかどうかを判定する。まだ確定していない場合、計画が確定した旬の次旬の初日、例えばＮ旬の計画が確定したならばＮ＋１旬の初日を立案更新日として更新し（ステップＳ１０９）、ステップＳ１０３に戻る。ステップＳ１０３から始まる次ループでは、計画が確定した旬（Ｎ旬）における在庫推移や船舶の運航状況を更新して、次旬（Ｎ＋１旬）の計画を確定させる。これを繰り返すことにより、計画作成期間（３ヶ月）分の計画が確定することになる（図２３を参照）。 (8) Planning start date update (step S109)
In step S108, it is determined whether or not plans for the plan creation period (3 months (9 months)) have been finalized. If it has not been confirmed yet, the first day of the next season in which the plan is confirmed, for example, if the plan for N season is confirmed, the first day of N + 1 season is updated as the plan update date (step S109), and the process returns to step S103. In the next loop starting from step S103, the inventory transition in the season (N season) when the plan is finalized and the operational status of the ship are updated to finalize the plan for the next season (N + 1 season). By repeating this, the plan for the plan creation period (3 months) is confirmed (see FIG. 23).
（９）配船計画の出力（ステップＳ１１０）
　以上のようにして作成した配船計画は、出力部２０５により、不図示のモニタに画面表示されたり、データベース４００を含む外部機器にデータ送信されたりする。 (9) Shipment plan output (step S110)
The ship allocation plan created as described above is displayed on a screen (not shown) by the output unit 205 or transmitted to an external device including the database 400.
　以上述べたように、マクロ最適化部２０２及びミクロ最適化部２０３では、まず入力データもしくは前ループから得られる、初期値（初期条件）に基づいて数式モデルを設定し、最適化計算を行い、シミュレータ２０１に対する指示を算出する。シミュレータ２０１は、計画確定期間（１旬）についてシミュレーションを終了すると、計画確定期間の最終状態での原材料の在庫推移、船舶の運航状況の推移の情報をマクロ最適化部２０２及びミクロ最適化部２０３に与える。マクロ最適化部２０２及びミクロ最適化部２０３は、その与えられた情報に基づいて数式モデルを設定し、最適化計算を行い、シミュレータに対する指示を算出する。このようにシミュレータ２０１と最適化部２０２、２０３を連動させることにより、計画作成期間（３ヶ月（９旬））の配船計画を作成することができる。 As described above, the macro optimization unit 202 and the micro optimization unit 203 first set a mathematical model based on the initial value (initial condition) obtained from the input data or the previous loop, perform optimization calculation, An instruction for the simulator 201 is calculated. When the simulator 201 completes the simulation for the plan finalization period (in the first season), the macro optimization unit 202 and the micro optimization unit 203 provide information on the transition of raw material inventory and the operational status of the ship in the final state of the plan finalization period. To give. The macro optimization unit 202 and the micro optimization unit 203 set a mathematical model based on the given information, perform optimization calculation, and calculate an instruction for the simulator. In this way, by linking the simulator 201 and the optimization units 202 and 203, it is possible to create a ship allocation plan for the plan creation period (3 months (9th September)).
　本実施形態に係る配船計画作成装置（方法）によれば、マクロ最適化部２０２及びミクロ最適化部２０３により行われた最適化計算の結果に基づいた計算指示をシミュレータ２０１（在庫推移シミュレータ、船舶運航状況推移シミュレータ）に出力する。このように、最適化計算の結果に基づいてシミュレーションが行われるものであるので、理論的な最適解を確実に得ることが可能となる。これにより、従来のようにシミュレーション結果を評価してシミュレーションを何回も繰り返して実行する必要がなく、シミュレーション結果を迅速かつ高精度に作成することができる。 According to the ship allocation plan creation apparatus (method) according to the present embodiment, the simulator 201 (the inventory transition simulator, the inventory transition simulator, the calculation instruction based on the result of the optimization calculation performed by the macro optimization unit 202 and the micro optimization unit 203). (Ship operation status transition simulator). As described above, since the simulation is performed based on the result of the optimization calculation, it is possible to surely obtain a theoretical optimum solution. Thereby, it is not necessary to evaluate the simulation result and repeat the simulation many times as in the prior art, and the simulation result can be created quickly and with high accuracy.
　また、シミュレータ２０１の規模が非常に大きい場合や制約条件が非常に多くて複雑な場合でも、シミュレータ２０１に記載された制約のうち、配船計画の作成に影響が大きい重要な部分のみを数式モデルに取り込むようにすることで、数式モデル設定部２０２ｂ、１０３ａの規模を適切な範囲にして、実用的な時間内で最適化計算を行うようにすることができる。シミュレータ２０１には、考慮すべき制約を全て記載することができるので、１回のシミュレーションを実行して作成された配船計画は現実に実行可能となることが保証される。 Further, even when the scale of the simulator 201 is very large or when the constraint conditions are very large and complicated, only the important part of the constraints described in the simulator 201 that has a large influence on the creation of the ship allocation plan is expressed by the mathematical model. Thus, the optimization calculation can be performed within a practical time by setting the scale of the mathematical model setting units 202b and 103a in an appropriate range. Since all the restrictions to be considered can be described in the simulator 201, it is guaranteed that the ship allocation plan created by executing one simulation can be actually executed.
　また、配船計画を作成する場合、ブラジル等の遠方より輸送される銘柄は、２ヶ月或いは3ヶ月に一度しか入荷されないといったこともあるため、長期間を考慮して配船計画を立てる必要がある。一方で、中国等頻繁に輸送される銘柄では数日で搬送される銘柄も存在する。更にバースの管理は、滞船料が発生することもあり、分単位で行われるため、分精度の計画が要求される。これらの要求に対して、マクロ最適化部２０２で船型（船の最大積載量を基に定義される船舶の大きさ）、船数、積揚地（積揚港）、積揚銘柄、積揚量、寄港順を選定し、ミクロ最適化部２０３で使用バース、入出港タイミングを選定するように演算の分担を行った。このため、負荷を抑えるとともに、高精度で求解可能となる。すなわち、マクロ最適化とミクロ最適化を連動させ、繰り返し実行することで、長期間（例えば３ヶ月）で特定する必要がある使用可能な船、積揚地、銘柄、量を長期間で考慮すると同時に、細かな時間精度が要求される使用バース、入出港タイミングは、短期間の（細かい）時間精度で最適化することを可能とした。 In addition, when preparing a ship allocation plan, brands transported from a distant place such as Brazil may be received only once every two or three months. is there. On the other hand, there are brands that are transported in a few days among brands that are frequently transported, such as China. Furthermore, management of berths is subject to berthing charges and is performed on a minute-by-minute basis, so a plan with minute accuracy is required. In response to these requirements, the macro optimization unit 202 sets the ship type (the size of the ship defined based on the maximum load capacity of the ship), the number of ships, the landing site (unloading port), the unloading brand, and unloading. The amount and port order were selected, and the calculation was divided so that the micro-optimization unit 203 could select the berth used and the port entry / exit timing. For this reason, the load can be suppressed and the solution can be obtained with high accuracy. In other words, when macro optimization and micro optimization are linked and executed repeatedly, it is possible to consider usable ships, landing sites, brands, and quantities that need to be specified over a long period (for example, 3 months) over a long period of time. At the same time, it is possible to optimize the use berth and entry / exit timing, which require fine time accuracy, with short (fine) time accuracy.
　また、配船計画作成の際に、船型（船の最大積載量を基に定義される船舶の大きさ）、船数、積揚地（積揚港）、積揚銘柄、積揚量をユーザが個別に固定できるようにしてもよい。例えば所定の船舶を使用する、所定の積港を利用する等が予め決まっているような事情もあるからである。特に、引取目標量として設定した量に基づいて配船計画を立案した後で、山元との交渉が進み、引取量が確定されるが、この際には、引取量、積地（積港）、積銘柄、積量（荷積量）は、契約の都合上変更することが許されない。しかし、揚地に関しては、在庫状況を判断して、揚地、揚銘柄、揚量を変更する余地が残される場合が多い。このため、積地（積港）、積銘柄、積量を一括して固定化できるような操作を可能にすれば、ユーザにとって利便性が高くなる。 Also, when preparing a ship allocation plan, the user can specify the ship type (the size of the ship defined based on the maximum load capacity of the ship), the number of ships, the landing site (shipping port), the loading brand, and the loading volume. May be fixed individually. This is because, for example, there are circumstances in which a predetermined ship is used or a predetermined loading port is used. In particular, after drafting a ship allocation plan based on the amount set as the target amount for collection, negotiations with Yamamoto will proceed and the amount will be finalized. The brand name and volume (loading volume) cannot be changed due to contractual reasons. However, with regard to the landing site, there is often a room for changing the landing site, the brand name, and the lifting amount after judging the stock status. For this reason, if operation which can fix a loading place (loading port), a loading brand, and a loading amount collectively is enabled, it will become convenient for a user.
　なお、ここまで述べた配船計画作成手法では、マクロ最適化部２０２の最適化計算部２０２ｃで輸送費用に関して構築された目的関数（評価関数）に基づいて最適化計算を行う例を説明したが、他の目的関数を加えてもよい。 In the ship allocation plan creation method described so far, the example in which the optimization calculation is performed based on the objective function (evaluation function) constructed for the transportation cost by the optimization calculation unit 202c of the macro optimization unit 202 has been described. Other objective functions may be added.
　例えば積地における負荷を平準化するために、同一の積地に入出港する船舶が同時期に集中したり、逆に船舶が入出港しない期間が続いたりすることを避ける、すなわち同一の積地では計画作成期間中にできるだけ均等に配船することが求められる。 For example, in order to level the load at the loading area, avoid the concentration of ships entering and leaving the same loading area at the same time, or conversely, the period when the ships do not enter or leave the port, that is, the same loading area. Therefore, it is required to allocate ships as evenly as possible during the planning period.
　そこで、図２４に示すように、積地毎に全銘柄の引取量を旬単位（或いは月単位）に集計し、それまでの累積を考える（引取量累積）。また、積地毎に全銘柄の引取目標量を旬単位（或いは月単位）に集計し、それまでの累積を目標値として設定する（引取目標量累積）。そして、引取量累積と引取目標量累積の差のミニマム化を目的とした目的関数を構築する。 Therefore, as shown in FIG. 24, the collected amounts of all the brands are summed in seasonal units (or monthly units) for each loading place, and the accumulation up to that point is considered (collected amount accumulation). In addition, the collection target amount of all brands is summed in seasonal units (or monthly units) for each loading place, and the accumulation up to that time is set as a target value (collection target amount accumulation). Then, an objective function is constructed for the purpose of minimizing the difference between the accumulated amount of collected items and the accumulated amount of collected items.
　これにより、各旬（或いは各月）で積地の引取量が均等に近づき、換言すれば、均等配船が可能になる。 This will allow the volume of loading at the season to approach each other evenly (or each month), in other words, evenly distribute ships.
　同様に、揚地における負荷を平準化するために、同一の揚地に入出港する船舶が同時期に集中したり、逆に船舶が入出港しない期間が続いたりすることを避ける、すなわち同一の揚地では計画作成期間中にできるだけ均等に配船することが求められる。 Similarly, in order to level the load at the landing site, it is avoided that the vessels entering and leaving the same landing site are concentrated at the same time, or conversely, the period when the vessels do not enter and leave the port continues. At the landing site, it is required to distribute ships as evenly as possible during the planning period.
　そこで、図２５に示すように、揚地毎に全銘柄の荷揚量を旬単位（或いは月単位）に集計し、それまでの累積を考える（荷揚量累積）。また、揚地毎に標準荷揚能力量を旬単位（或いは月単位）に集計し、それまでの累積を目標値として設定する（揚地標準荷揚能力量累積）。そして、その差を残荷揚量と定義し、この残荷揚量のミニマム化を目的とした目的関数を構築する。 Therefore, as shown in FIG. 25, the amount of unloading of all brands is summed in seasonal units (or monthly units) for each landing site, and the accumulation up to that point is considered (unloading amount accumulation). In addition, the standard unloading capacity for each landing site is summed in seasonal units (or monthly units), and the accumulation up to that time is set as a target value (cumulative standard unloading capacity accumulation). Then, the difference is defined as the remaining unloading amount, and an objective function for the purpose of minimizing the remaining unloading amount is constructed.
　これにより、各旬（或いは各月）で揚地の荷揚量が均等に近づき、換言すれば、均等配船が可能になる。即ち、マクロ最適化においても、滞船を抑制することが可能となる。 This will allow the unloading capacity of the landing site to approach evenly in each season (or each month), in other words, evenly ship. That is, even in macro optimization, it becomes possible to suppress a stagnation.
　以上により、複数銘柄の原材料を入荷して混合する配合計画、及び複数銘柄の原材料を複数の積地から複数の揚地に輸送する配船計画を相互に連係させて一括で作成することが可能となる。また、これら計画においては、配合計画・配船計画を通して全体として輸送費用をミニマム化することが可能となる。 By the above, it is possible to create a batch plan by linking each other with a blending plan for receiving and mixing multiple brands of raw materials and a ship allocation plan for transporting multiple brands of raw materials from multiple loading sites to multiple landing sites It becomes. Moreover, in these plans, it becomes possible to minimize the transportation cost as a whole through the blending plan / ship allocation plan.
（第２の実施形態）
　第２の実施形態として、第２の配合計画作成装置３００を導入する例を説明する。第２の配合計画作成装置３００は、配船計画作成装置２００により作成された配船計画に基づいて、複数銘柄の配合原材料を入荷して混合する配合計画を作成する。なお、第２の配合計画作成装置３００の構成や基本的な処理動作は第１の配合計画作成装置１００と同様であり、ここでも図２～１１を参照して説明する。 (Second Embodiment)
As a second embodiment, an example in which the second formulation plan creation device 300 is introduced will be described. The second blending plan creation apparatus 300 creates a blending plan for receiving and mixing a plurality of branded blending raw materials based on the dispatching plan created by the dispatching plan creation apparatus 200. The configuration and basic processing operations of the second blending plan creation apparatus 300 are the same as those of the first blending plan creation apparatus 100, and will be described here with reference to FIGS.
　図２は、第２の配合計画作成装置３００を含むシステム構成例を示す図である。図２に示すように、第２の配合計画作成装置３００は、配合計画を作成するに際して、配合計画を立案する上で必要となる以下のような制約条件、前提条件のデータを操業者が設定するか、或いはデータベース４００から取り込む：計画作成期間、原材料の入荷予定（配船計画作成装置２００により作成された配船計画による入荷量）、原材料の在庫状況、原材料の性状、原材料の単価を表す購入費用情報、船舶を利用する際の輸送費用情報を含む。 FIG. 2 is a diagram showing a system configuration example including the second blending plan creation device 300. As shown in FIG. 2, when creating the blending plan, the second blending plan creation device 300 sets the following constraint and precondition data necessary for formulating the blending plan by the operator. Or from the database 400: Represents the plan creation period, the arrival schedule of raw materials (the amount of arrival by the ship allocation plan created by the ship allocation plan creation device 200), the stock status of the raw materials, the properties of the raw materials, and the unit price of the raw materials Includes purchase cost information and transportation cost information when using a ship.
　第２の配合計画作成装置３００は、多種類（複数銘柄）の原材料を入荷して混合する混合計画を、シミュレーションを実行して作成する。この混合計画は、原材料の需給バランス制約、混合後の性状制約を満たすような、各銘柄の使用量（配合割合）を含む。第２の配合計画作成装置３００では、後述するように、ＬＰ（線形計画法）、ＭＩＰ（混合整数計画法）、ＱＰ（２次計画法）等の数理計画法に則って構築された、原材料の需給バランス制約を表す数式モデル（「需給バランスモデル」とも称する）、及び、混合後の性状制約を表す数式モデル（「性状モデル」とも称する）を、各条件に沿って設定し、求解することにより配合計画の最適化を図る。 The second blending plan creation device 300 creates a mixing plan for receiving and mixing various types (multiple brands) of raw materials by executing a simulation. This mixing plan includes the usage amount (mixing ratio) of each brand so as to satisfy the supply-demand balance constraint of raw materials and the property constraint after mixing. In the second blending plan creation device 300, as will be described later, raw materials constructed in accordance with mathematical programming methods such as LP (Linear Programming), MIP (Mixed Integer Programming), QP (Secondary Programming), etc. Set up and solve a mathematical model that expresses supply and demand balance constraints (also referred to as “demand balance model”) and a mathematical model that expresses mixed property constraints (also referred to as “property model”) according to each condition. To optimize the formulation plan.
　ここで、第１の配合計画作成装置１００では、入荷量は、入荷量の累積量が引取目標累積量と、大幅に外れないように、配合計画を作成した。しかし、第２の配合計画作成装置３００において、入荷量は、配船計画作成装置２００により船舶毎の荷揚量として予め決定されているため、既知の量となる。このため、第２の配合計画作成装置３００では、入荷量は求める量ではなく、固定されている量となる。また、上記理由により引取目標量を考慮する必要はない。 Here, in the first blending plan creation apparatus 100, the blending plan was created so that the accumulated amount of the received amount did not significantly deviate from the collection target accumulated amount. However, in the second blending plan creation device 300, the arrival amount is a known amount because it is determined in advance by the ship allocation plan creation device 200 as an unloading amount for each ship. For this reason, in the 2nd mixing | blending plan preparation apparatus 300, the amount of arrival becomes not the quantity calculated | required but the quantity fixed. Further, it is not necessary to consider the take-up target amount for the above reason.
　また、第１の実施形態においては、第１の配合計画作成装置１００により作成された使用計画を、配船計画作成装置２００の入力データとして、配船計画を作成している。この場合、第１の配合計画作成装置１００において、引取目標累積量に基づいて計画を作成している。このとき、例えば引取目標累積量が原材料Ｘに対して、５万トンと設定されており、使用計画、入荷計画共に引取目標累積量と同じ５万トンが累積量として、計画された場合を考える。上記使用計画を配船計画作成装置の入力データとして使用した場合、船舶リストに登録された船の最大積載量が４．５万トンの船１隻しか存在しない場合に、入荷量は４．５万トンとなり、下記の如く、在庫切れを発生する可能性がある。
　　在庫量＝４．５万トン　－　５万トン　＝　－０．５万トン Further, in the first embodiment, the shipping plan is created using the use plan created by the first blending plan creating device 100 as input data of the shipping plan creating device 200. In this case, the first formulation plan creation device 100 creates a plan based on the take-up target cumulative amount. At this time, for example, the case where the take-up target cumulative amount is set to 50,000 tons with respect to the raw material X, and the use plan and the arrival plan are planned as a cumulative amount of 50,000 tons that is the same as the take-up target cumulative amount. . When the above usage plan is used as input data for the ship allocation plan creation device, if there is only one ship with a maximum load capacity of 45,000 tons registered in the ship list, the arrival quantity is 4.5 There is a possibility of out of stock as shown below.
Inventory volume = 45,000 tons-50,000 tons =-55,000 tons
　上記の例は極端な例ではあるが、第１の配合計画作成装置１００では、引取目標量を基に配合計画を作成し、作成された使用計画を基に配船計画を作成しているため、引取目標量と船舶の最大積載量がマッチしていない場合、上記の例のような現象は起こりえる。 Although the above example is an extreme example, the first blending plan creation device 100 creates a blending plan based on the take-up target amount, and creates a ship allocation plan based on the created usage plan. When the take-up target amount and the maximum load capacity of the ship do not match, the phenomenon as in the above example can occur.
　そこで、より良い配合計画を作成するために、引取目標量ではなく、配船計画作成装置１００により決定された配船計画を入力データとして、第２の配合計画作成装置３００により、配合計画を修正する。これにより、上記のような微妙な引取目標量と船舶の最大積載量がマッチしていないために発生する可能性のある在庫切れを防ぐことが可能となる。
　本実施形態では、船舶の運賃は考慮するが、バースでの着岸時刻など詳細な運航状況まで考慮することなく、引取目標量を元に第１の配合計画作成装置により計画された使用量を、配船計画側の在庫推移シミュレータ、及び船舶運航状況推移シミュレータによるシミュレーションの結果に現れる、詳細な配船事情まで考慮して、配合計画を再度更新できる。この作用により、配合計画の精度の向上に大きな効果が得られる。 Therefore, in order to create a better blending plan, the blending plan is corrected by the second blending plan creation device 300 using, as input data, the dispatching plan determined by the dispatching plan creation device 100, not the take-up target amount. To do. As a result, it is possible to prevent out of stock that may occur because the delicate take-up target amount and the maximum load capacity of the ship do not match.
In this embodiment, the fare of the ship is taken into account, but the usage amount planned by the first combination plan creation device based on the take-off target amount without taking into account the detailed operation status such as the berthing time at the berth, The formulation plan can be updated again in consideration of the detailed ship allocation situation that appears in the results of the simulation by the inventory transition simulator on the ship allocation plan side and the ship operation status transition simulator. By this action, a great effect is obtained in improving the accuracy of the blending plan.
　表示部３０３では、第２の配合計画作成装置３００で求められた各銘柄の使用量（配合割合）、入荷量、在庫推移グラフ、各種帳票を表示する。 In the display unit 303, the usage amount (mixing ratio) of each brand obtained by the second blending plan creation device 300, the amount received, a stock transition graph, and various forms are displayed.
　操業者評価部３０４では、求められた配合計画を様々な観点（例えば、在庫推移、性状等）から操業者が評価し、満足のいく結果でなければ、必要に応じて配合割合等を修正する。その際に、必要に応じて目的関数の重みや評価の指標を変えたり、数式モデルを設定する対象期間・計画確定期間を変えたりする。また、全部の或いは指定した処理のみ使用量の固定をする等、操業者の意志を反映させた数式モデルの設定が可能である。そして、第２の配合計画作成装置３００で再度配合計画を作成し直す。 In the operator evaluation unit 304, the operator evaluates the obtained blending plan from various viewpoints (for example, inventory transition, properties, etc.), and corrects the blending ratio and the like as necessary if the result is not satisfactory. . At that time, the weight of the objective function and the evaluation index are changed as necessary, and the target period / plan decision period for setting the mathematical model is changed. In addition, it is possible to set a mathematical model reflecting the operator's will, such as fixing the amount of use only for all or specified processes. Then, the blending plan is created again by the second blending plan creation device 300.
　図３は、第２の配合計画作成装置の基本的な構成を示すブロック図である。図３に示すように、第２の配合計画作成装置３００は、シミュレータ（在庫推移シミュレータ３１１、性状シミュレータ３１２を含む）、本発明でいう数式モデル設定部として機能するモデル設定部（需給バランスモデル設定部３１３、性状モデル設定部３１４）、本発明でいう最適化計算部として機能する計画部３１５を含んで構成され、更に入出力部を併せ持つ。 FIG. 3 is a block diagram showing a basic configuration of the second formulation planning device. As shown in FIG. 3, the second blending plan creation apparatus 300 includes a simulator (including an inventory transition simulator 311 and a property simulator 312), a model setting unit (demand / supply balance model setting) that functions as a mathematical model setting unit in the present invention. Unit 313, property model setting unit 314), and a plan unit 315 functioning as an optimization calculation unit in the present invention, and further includes an input / output unit.
　在庫推移シミュレータ３１１は、各原材料の需給状態（在庫推移）を計算するシミュレータである。性状シミュレータ３１２は、原材料を混合した後の性状を計算するシミュレータである。在庫推移シミュレータ３１１、性状シミュレータ３１２が互いに連動することで、原材料の在庫推移、混合後の性状を計算する。 The inventory transition simulator 311 is a simulator for calculating the supply and demand state (inventory transition) of each raw material. The property simulator 312 is a simulator that calculates properties after mixing raw materials. The inventory transition simulator 311 and the property simulator 312 work together to calculate the inventory transition of raw materials and the properties after mixing.
　本実施形態においては、例えば以下の情報を含む、入力データ３１６に基づいて数式モデルの設定処理を行う：計画作成期間、原材料の入荷予定、原材料の在庫状況、原材料の性状、原材料の単価を表す購入費用情報、及び船舶を利用する際の輸送費用情報。配合計画の立案開始日時から予め設定された最適化期間分を対象として、予め設定した時間精度に基づいて、ＬＰ（線形計画法）、ＭＩＰ（混合整数計画法）、ＱＰ（２次計画法）等の数理整数計画法等に則り、需給バランスモデル設定部３１３にて需給バランス制約（在庫制約）を表す数式モデルが設定され、性状モデル設定部３１４により性状制約を表す数式モデルが設定される。 In this embodiment, for example, a mathematical model setting process is performed based on input data 316 including the following information: a plan creation period, a raw material arrival schedule, a raw material inventory status, a raw material property, and a raw material unit price. Purchase cost information and transportation cost information when using a ship. LP (Linear Programming), MIP (Mixed Integer Programming), QP (Secondary Programming) based on the preset time accuracy for the optimization period set in advance from the formulation planning start date and time In accordance with mathematical integer programming such as, a mathematical model representing supply / demand balance constraints (inventory constraints) is set by the supply / demand balance model setting unit 313, and a mathematical model representing property constraints is set by the property model setting unit 314.
　需給バランスモデル設定部３１３、性状モデル設定部３１４により設定された数式モデルを用いて、在庫を切らさないようにするとともに要求される性状を満足し、かつ、費用（原材料の購入費用及び輸送費用）をミニマム化して配合計画を作成するように、計画部３１５により最適化計算を行う。この最適化計算の結果に基づいて、在庫推移シミュレータ３１１、性状シミュレータ３１２に対する計算指示を算出する。この計算指示を受けて、在庫推移シミュレータ３１１が在庫推移をシミュレートし、性状シミュレータ３１２が計画に従って製造される製品・半製品の性状をシミュレートする。例えば、鉄鋼業においては、石炭を混合して焼き固めたコークス（製品）、鉄鉱石を還元して得られる銑鉄を精錬した溶鋼を凝固させたスラブ（半製品）等の性状をシミュレートする。 Using mathematical formula models set by the supply and demand balance model setting unit 313 and the property model setting unit 314, the inventory is not cut and the required properties are satisfied, and the cost (raw material purchase cost and transportation cost) The planning unit 315 performs optimization calculation so as to create a blending plan by minimizing. Based on the result of this optimization calculation, calculation instructions for the inventory transition simulator 311 and the property simulator 312 are calculated. In response to this calculation instruction, the inventory transition simulator 311 simulates the inventory transition, and the property simulator 312 simulates the properties of the products and semi-finished products manufactured according to the plan. For example, in the iron and steel industry, properties such as coke (product) that is baked and hardened by mixing coal, and slab (semi-finished product) that is obtained by solidifying molten steel obtained by refining pig iron obtained by reducing iron ore are simulated.
　かかる第２の配合計画作成装置３００によれば、従来のように予め決められたルールに基づいて計算指示が行われるのではなく、計画部３１５により行われた最適化計算の結果に基づいた計算指示を在庫推移シミュレータ３１１、性状シミュレータ３１２に出力する。このため、そのときの事象に応じた最適な計算指示を確実に行うことが可能となる。 According to the second blending plan creation device 300, calculation is not performed based on a predetermined rule as in the prior art, but calculation based on the result of optimization calculation performed by the planning unit 315. The instruction is output to the inventory transition simulator 311 and the property simulator 312. For this reason, it is possible to reliably perform an optimal calculation instruction according to the event at that time.
　また、例えば、図７に示すように予め設定された計画確定期間分について、在庫推移シミュレータ３１１、性状シミュレータ３１２によるシミュレーションが、行われる。またこのシミュレーションが終了すると、立案開始日が更新され、更新前の計画確定期間の最終状態、つまり更新後の立案開始日での在庫推移、性状の情報に基づいて、新たな最適化期間分の需給バランスモデル設定部３１３により在庫制約を表す数式モデルが設定され、性状モデル設定部３１４により性状制約を表す数式モデルが設定され、計画部３１５に与えられる。この在庫推移、性状の情報が与えられると、計画部３１５は最適化計算を実行する。 Further, for example, as shown in FIG. 7, a simulation by the inventory transition simulator 311 and the property simulator 312 is performed for a predetermined plan fixed period. At the end of this simulation, the planning start date is updated, and based on the final state of the plan finalization period before the update, that is, inventory transition and property information at the planning start date after the update, the new optimization period A mathematical model representing inventory constraints is set by the supply / demand balance model setting unit 313, and a mathematical model representing property constraints is set by the property model setting unit 314, and is given to the planning unit 315. Given the inventory transition and property information, the planning unit 315 executes optimization calculation.
　第２の配合計画作成装置３００においても、第１の実施形態で述べたように、シミュレータ（在庫推移シミュレータ３１１、性状シミュレータ３１２）とモデル設定部（需給バランスモデル設定部３１３、性状モデル設定部３１４）と計画部３１５とを連動させて配合計画を作成するようにした。このため以下の作用効果が得られる。（１）シミュレーションを繰り返して実行せずに配合計画を作成することができる。（２）配合計画作成に影響が大きい重要な部分のみを計画部３１５に取り込むようにすることで計算時間を短縮することができるとともに、（３）大規模問題を解くことが可能になる。 Also in the second blending plan creation device 300, as described in the first embodiment, the simulator (the inventory transition simulator 311 and the property simulator 312) and the model setting unit (the supply and demand balance model setting unit 313, the property model setting unit 314). ) And the planning unit 315 are linked to create a blending plan. For this reason, the following effects are obtained. (1) A blending plan can be created without repeatedly executing a simulation. (2) Calculation time can be shortened by incorporating only important parts that have a large influence on the formulation plan creation into the planning unit 315, and (3) large-scale problems can be solved.
　以下、第２の配合計画作成装置３００の構成及びこの装置を用いて実行する配合計画作成方法の各ステップをより詳細に説明する。図４は、図３を用いて説明した第２の配合計画作成装置３００の基本的な構成に対する、配合計画作成装置の詳細な構成を示す図である。また、図５は、この装置を用いて実行する配合計画作成方法の各ステップを示すフローチャートである。 Hereinafter, the configuration of the second blending plan creation device 300 and each step of the blending plan creation method executed using this device will be described in more detail. FIG. 4 is a diagram illustrating a detailed configuration of the blending plan creation apparatus with respect to the basic configuration of the second blending plan creation apparatus 300 described with reference to FIG. 3. FIG. 5 is a flowchart showing each step of the formulation plan creation method executed using this apparatus.
　配合計画作成の概要として、例えば図６に示すように、、以下のような計算、調整の工程が含まれる：複数ある製鉄所（揚港）ａ～ｃでの原材料（銘柄）の需給バランスを取ること；（各銘柄Ａ～Ｎの在庫を切らさない等）要求される性状を満足させること；かつ、費用（原材料の購入費用及び輸送費用）をミニマム化すること；前記の条件を満たすような配合計画として製鉄所ａ～ｃ毎の各銘柄Ａ～Ｎの使用量（配合割合）、入荷量を決定すること。ここで、製鉄所毎に使用量の合計量である予定使用量は、入力データとして与えられる。このため、配合割合（％）＝使用量／予定使用量×１００となる。このため、使用量、配合割合の一方が決定されれば、他方が決定されることとなる。 For example, as shown in FIG. 6, the outline of formulation planning includes the following calculation and adjustment processes: Supply and demand balance of raw materials (brands) at a plurality of steelworks (lifting ports) a to c To satisfy the required properties; and to minimize costs (raw material purchase and transportation costs); to satisfy the above conditions As a blending plan, determine the usage (mixing ratio) and receipt of each brand A to N for each steelworks a to c. Here, the planned usage amount, which is the total usage amount for each steelworks, is given as input data. Therefore, the blending ratio (%) = used amount / planned used amount × 100. For this reason, if one of usage-amount and a mixture ratio is determined, the other will be determined.
（１）入力データの取込み（図４の入力データ取込み部３５１、図５のステップＳ３０１）
　本処理に必要な情報（原材料の入荷予定、原材料の在庫状況、原材料の性状、原材料の単価を表す購入費用情報、船舶を利用する際の輸送費用情報等）をオンラインにて読み込み、必要に応じて操業者が修正を加える。 (1) Acquisition of input data (input data acquisition unit 351 in FIG. 4, step S301 in FIG. 5)
Information required for this processing (scheduled arrival of raw materials, raw material stock status, raw material properties, purchase cost information indicating the unit price of raw materials, transportation cost information when using a ship, etc.) is read online and as required The operator makes corrections.
　ここで、入力データ取込み部３５１により取り込まれる原材料の入荷予定には、配船計画（船舶１隻毎の積港、積港着の日時、積銘柄、積量、揚港、揚港着の日時、揚銘柄、揚量を含む項目について計画）といった、入荷量を表す情報が含まれる。例えば、図２６に示す配船計画では、図１３に示したような船舶リストにリストアップされている各船舶の運航予定が組まれている。配船計画では、船舶リストにリストアップされた船舶について、積港、積港着の日時、銘柄、積量、揚港、揚港着の日時、銘柄、揚量を含む項目について計画が立案されている。 Here, the arrival schedule of the raw materials taken in by the input data fetching unit 351 includes the ship allocation plan (shipping port for each vessel, arrival date / time of arrival at the loading port, brand name, loading volume, arrival / departure date / time at arrival at the arrival / disembarkation port) , Information on the amount received, such as a plan for an item including a lifted brand and a lifted amount). For example, in the ship allocation plan shown in FIG. 26, the operation schedule of each ship listed in the ship list as shown in FIG. 13 is set. In the ship allocation plan, a plan is formulated for the items listed in the ship list, including items including loading / unloading date / time, brand, volume, landing / unloading date / time, brand / lift. ing.
　例えば、図２６に示す配船計画では、連続航海船Ａの航海Ｎｏ．１は、２００７年１１月１９日２１時に積港（Ｘ１港）沖に着き、２００７年１２月１３日２１時に積港（Ｘ１港）のコード「１」で表されるバースに着岸し、２００７年１２月１４日２１時に積港（Ｘ１港）を出港する。この際に原材料の銘柄Ａを４００００トン、銘柄Ｂを３５０００トン積載する。その後、１６９８０分航海して、２００７年１２月２６日１６時に揚港（Ａ港）沖に着き、２００７年１２月２７日１時に揚港（Ａ港）のコード「４」で表されるバースに着岸し、２００７年１２月２８日１６時に揚港（Ａ港）を出港する。この際に原材料の銘柄Ａを２５０００トン、銘柄Ｂを１５０００トン荷揚げする。その後、３０６０分航海して、２００７年１２月３０日１９時に揚港（Ｂ港）沖に着き、２００７年１２月３０日１９時に揚港（Ｂ港）のコード「１３」で表されるバースに着岸し、２００８年１月１日２３時に揚港（Ｂ港）を出港する。この際に原材料の銘柄Ａを１５０００トン、銘柄Ｂを２００００トン荷揚げする。上記例示した連続航海船Ａの航海Ｎｏ．１においては、揚港（Ａ港）に２００７年１２月２７日、原材料の銘柄Ａを２５０００トン、銘柄Ｂを１５０００トン、揚港（Ｂ港）に２００７年１２月３０日、原材料の銘柄Ａを１５０００トン、銘柄Ｂを２００００トン、入荷したとする、配合計画作成装置の入力データとなる。 For example, in the ship allocation plan shown in FIG. 1 arrived off the port of loading (X1 port) at 19:00 on November 19, 2007, and arrived at the berth represented by code “1” of the port of loading (X1 port) at 21:00 on December 13, 2007. Depart from Sekiko Port (X1 Port) at 21:00 on December 14, 1980. At this time, 40000 tons of brand A of raw materials and 35000 tons of brand B are loaded. After that, sailed for 16980 minutes, arrived at the offshore of Yangon (Port A) at 16:00 on December 26, 2007, and arrived at the berth represented by code “4” at Yangon (A) on December 27, 2007 Docked at 16:00 on December 28th, 2007 at 18:00. At this time, 25,000 tons of brand A of raw materials and 15000 tons of brand B are unloaded. After that, sailed 3060 minutes, arrived at the offshore of Yangon (Port B) at 19:00 on December 30, 2007, and reached the berth represented by the code “13” of Yangon (Port B) at 19:00 on December 30, 2007 Berthing and leaving the port (B port) at 13:00 on January 1, 2008. At this time, 15,000 tons of brand A of raw materials and 20000 tons of brand B are unloaded. The cruise No. of the continuous cruise ship A exemplified above. In No. 1, on December 27, 2007 in Yangon (Port A), 25,000 tons of brand A of raw materials, 15,000 tons of brand B, and on December 30, 2007, in Yangon (Port B). 15000 tons and Brand B of 20000 tons are received, it becomes the input data of the formulation plan creation device.
　原材料の在庫状況、原材料の性状、原材料の単価を表す購入費用情報については、第１の実施形態で説明した第１の配合計画作成装置１００の場合と同様である。 The purchase status information indicating the stock status of raw materials, the properties of the raw materials, and the unit price of the raw materials is the same as in the case of the first blending plan creating apparatus 100 described in the first embodiment.
　また、船舶を利用する際の輸送費用情報については、配船計画作成装置２００により、将来に亘り計画された配船計画が、第２の配合計画作成装置３００に入力データとして渡される。このため、銘柄別・揚港別見做しフレートを使用する必要はなく、船舶別・積港別・揚港別フレートを使用することができる。 In addition, regarding the transportation cost information when using the ship, the ship allocation plan creation device 200 passes the ship allocation plan planned for the future to the second combination plan creation device 300 as input data. For this reason, it is not necessary to use the freight by brand / shipping port, and the freight by ship / shipping port / shipping port can be used.
　以上説明した入力データ取込み部３５１及びステップＳ３０１が、本発明でいう第２の配合計画作成部のデータ取込み部及びそれによる処理の例である。 The above-described input data fetching unit 351 and step S301 are examples of the data fetching unit of the second blending plan creating unit and processing by it in the present invention.
（２）配合計画作成期間の設定（図４の計画作成期間設定部３５２、図５のステップＳ３０２）
　配合計画を作成する期間を設定する。この作成期間は立案者の必要に応じて任意の期間を設定可能とする。ここでは、一例として１０日間分を立案する。 (2) Setting of formulation plan creation period (plan creation period setting unit 352 in FIG. 4, step S302 in FIG. 5)
Set the period for creating a recipe. This creation period can be set as desired according to the planner's needs. Here, 10 days is planned as an example.
（３）配合計画作成時間精度の設定（図４の時間精度設定部３５３、図５のステップＳ３０３）
　配合計画を作成する時間精度並びにシミュレーション精度を設定する。この時間精度並びにシミュレーション精度は、立案者の必要に応じて個別に任意の精度を設定可能とする。例えば立案の細かな精度を必要とする計画作成期間の前半では精度を細かくし、粗い計画で十分な計画作成期間の後半では精度を粗くすることで、十分な精度と短い計算時間での効率的な計画作成が可能になる。 (3) Setting of mixing plan creation time accuracy (time accuracy setting unit 353 in FIG. 4, step S303 in FIG. 5)
Set the time accuracy and simulation accuracy to create a recipe. The time accuracy and the simulation accuracy can be arbitrarily set individually according to the planner's needs. For example, by making the precision fine in the first half of the planning period that requires fine planning accuracy, and by making the precision coarse in the second half of the sufficient planning period with a rough plan, it is efficient with sufficient precision and short calculation time. Planning is possible.
（４）最適化期間の設定（図４の最適化期間設定部３５４、図５のステップＳ３０４）
　配合計画を作成する最適化期間を設定する。この最適化期間は立案者の必要に応じて個別に任意の対象期間を設定可能とする。ここでは、一例として計画作成期間を通して、最適化期間を３日間とする。 (4) Optimization period setting (optimization period setting unit 354 in FIG. 4, step S304 in FIG. 5)
Set the optimization period for creating a recipe. This optimization period can be set to any target period individually as required by the planner. Here, as an example, the optimization period is 3 days throughout the planning period.
（５）計画確定期間の設定（図４の計画確定期間設定部３５５、図５のステップＳ３０５）
　配合計画を確定する計画確定期間を設定する。この計画確定期間は、立案者の必要に応じて個別に任意の期間を設定可能とする。例えば、立案の細かな精度を必要とする計画作成期間の前半では計画確定期間を短くし、粗い計画で十分な計画作成期間の後半では計画確定期間を長くする。このことで、十分な精度を持ちながら短い計算時間で、効率的な計画作成が可能になる。ここでは、一例として、計画確定期間を１日に設定する。この場合は、数式モデルに対する解に基づいてシミュレートした結果得られる配合計画に対しては、計画作成期間を通して最初の１日分を確定する。 (5) Setting of plan fixed period (plan fixed period setting unit 355 in FIG. 4, step S305 in FIG. 5)
Set the plan confirmation period to finalize the recipe. This plan decision period can be set to an arbitrary period individually as required by the planner. For example, the plan finalization period is shortened in the first half of the plan creation period that requires fine planning accuracy, and the plan finalization period is lengthened in the second half of the plan preparation period sufficient for a rough plan. This makes it possible to create an efficient plan in a short calculation time with sufficient accuracy. Here, as an example, the plan confirmation period is set to one day. In this case, for the blending plan obtained as a result of simulation based on the solution for the mathematical model, the first day is determined throughout the plan creation period.
（６）配合計画の需給バランス制約を数式モデルに設定（図３の需給バランスモデル設定部３１３、図４の需給バランスモデル設定部３５６、図５のステップＳ３０６）
　入力データ取込み部３５１により取込まれたデータの全部又は一部に基づいて、設定された最適化期間分を設定された時間精度で需給バランス制約に基づいて数式モデルを設定する。 (6) Supply / demand balance constraints of the composition plan are set in the mathematical model (supply / demand balance model setting unit 313 in FIG. 3, supply / demand balance model setting unit 356 in FIG. 4, step S306 in FIG. 5).
Based on all or a part of the data fetched by the input data fetching unit 351, the mathematical model is set based on the supply-demand balance constraint with the set time accuracy for the set optimization period.
　第１の配合計画作成装置１００の場合と同様に、各銘柄の在庫量が一定の安全在庫量と呼ばれる値以上であることが要求される。この場合の制約は、上記の（式４）と表される。 As in the case of the first blending plan creation device 100, the stock amount of each brand is required to be equal to or greater than a value called a certain safety stock amount. The constraint in this case is expressed as (Equation 4) above.
　また、各銘柄の在庫量は、前日の在庫量、前日の入荷量、前日の使用量より決定される。この場合の関係式を表す制約は、上記の（式５）と表される。つまり、当日の在庫量は、前日の在庫量と当日に入荷（荷揚）する量を足した値から、当日の使用量を引いた値となる。 In addition, the stock quantity of each brand is determined from the inventory quantity of the previous day, the arrival quantity of the previous day, and the usage quantity of the previous day. The constraint representing the relational expression in this case is expressed as the above (Formula 5). In other words, the stock quantity on the current day is a value obtained by subtracting the use quantity on the current day from the value obtained by adding the stock quantity on the previous day and the quantity received (unloaded) on the current day.
　また、各銘柄の使用量のある日の合計は、当該日の全銘柄合計に対して予定された使用量と一致する必要がある。この場合の関係を表す制約式は、上記の（式６）と表される。 Also, the total amount of usage for each brand on a certain day must match the planned usage for all brands on that day. The constraint equation representing the relationship in this case is expressed as (Equation 6) above.
　また、各種原材料の購買に対する要因等に鑑み、操業者は、目標とする配合割合を設定し、この与えた目標とする配合割合に近い配合割合を実現する配合計画が作成されることを求める。つまり配合割合が操業者の想定と大きくかけ離れると、想定した購買量を満たせなくなったり、購買量を越えたり、また操業設備に無理な操業を及ぼすことが想定される。このため、目標として与えた配合割合に近い配合割合が出力されることが必要となる。上記機能を実現するための制約を以下に示す。つまり、銘柄の使用量から使用目標量（目標とする配合割合）（定数）を引いた値を、使用目標量からの溢れ量の変数として定義する。ここで、使用量と使用目標量が近い量を取る程、良い計画であるため、この溢れ量は少ない程良い。上記理由のため、後述するように、この溢れ量が、目的関数の項目として追加され、最適化によってミニマム化される。同様に銘柄の使用目標量から使用量を引いた値を、使用目標量からの不足量の変数として定義する。ここで、使用量と使用目標量は近い量を取る程良い計画であるため、この不足量は少ない程良い。上記理由のため、後述するようにこの不足は、目的関数の項目として追加され、最適化によってミニマム化される。この場合、各銘柄の使用量、使用目標量、溢れ量、不足量との関係を表す制約式は上記の（式７）と表される。つまり、溢れが生じる場合は使用量から溢れ量を引き、不足が生じる場合は不足量を足すと、使用目標量と一致する。 Further, in view of factors for purchasing various raw materials, the operator sets a target blending ratio and requests that a blending plan that realizes a blending ratio close to the given blending ratio is created. In other words, if the blending ratio is far from the operator's assumption, it is assumed that the assumed purchase amount cannot be satisfied, the purchase amount is exceeded, or the operation facility is unreasonably operated. For this reason, it is necessary to output a blending ratio close to the blending ratio given as a target. The restrictions for realizing the above functions are shown below. That is, a value obtained by subtracting the target usage amount (target mixture ratio) (constant) from the brand usage amount is defined as a variable of the overflow amount from the usage target amount. Here, the closer the usage amount to the usage target amount, the better the plan. Therefore, the smaller the overflow amount, the better. For the above reason, as described later, this overflow amount is added as an item of the objective function, and is minimized by optimization. Similarly, a value obtained by subtracting the use amount from the use target amount of the brand is defined as a variable of the shortage amount from the use target amount. Here, since the plan is such that the usage amount and the usage target amount are close to each other, the smaller the shortage amount, the better. For the above reason, as described later, this shortage is added as an item of the objective function and is minimized by optimization. In this case, the constraint equation representing the relationship between the usage amount, the usage target amount, the overflow amount, and the shortage amount of each brand is expressed as the above (Equation 7). That is, if overflow occurs, the overflow amount is subtracted from the usage amount, and if there is a shortage, the shortage amount is added to match the target usage amount.
　更に、前日の配合割合とその翌日の配合割合が大きく乖離すると、操業に困難を来たす。つまり、別原材料を使用するための段取り時間の増加や、設備の故障の原因となる。このため、前日の配合割合とその翌日の配合割合が大きく乖離することがない配合計画が、作成されることを求める。これを実現するための制約を上記の（式９）に示す。 Furthermore, if the blending ratio of the previous day and the blending ratio of the next day are greatly different, operation will be difficult. That is, it causes an increase in setup time for using another raw material and a failure of equipment. For this reason, it is calculated | required that the mixing | blending plan in which the mixing | blending ratio of the previous day and the mixing ratio of the following day do not largely diverge is created. The constraints for realizing this are shown in (Equation 9) above.
　なお、上述した需給バランス制約は一例であり、他の制約に替えたり、他の制約を加えたりしてもよい。 Note that the above-described supply-demand balance constraint is an example, and other constraints may be substituted or other constraints may be added.
（７）配合計画の性状制約を用いて数式モデルを設定（図３の性状モデル設定部３１４、図４の線形化部３５７ａを含む性状モデル設定部３５７、図５のステップＳ３０７、Ｓ３０７ａ）
　入力データ取込み部３５１により取込まれたデータの全部又は一部に基づいて、設定された最適化期間及び時間精度を用いて、性状制約を用いて数式モデルを設定する。鉄鉱石の配合計画を作成する場合、性状としてはに用いられる原材料の性状としては、例えば、以下のものが挙げられる：鉄分、SiO2、Al2O3、SiO2、等。石炭の配合計画を作成する場合の性状としては、、以下のものが挙げられる：ＣＳＲ（熱間反応後強度）、ＤＩ（コークス強度）、ＶＭ（揮発分）、膨張圧等。これらの性状が、要求される性状制約を満たす必要がある。混合後の性状モデルの一例を上記の（式１４）に示した。なお、（式１４）では下限値Ｓを有する例を示すが、上限値を有する場合、上限値及び下限値の両方を有する場合もありうる。 (7) A mathematical model is set using the property constraint of the blending plan (the property model setting unit 314 in FIG. 3, the property model setting unit 357 including the linearization unit 357a in FIG. 4, and steps S307 and S307a in FIG. 5).
Based on the whole or part of the data fetched by the input data fetching unit 351, the mathematical model is set using the property constraints using the set optimization period and time accuracy. When preparing a composition plan for iron ore, the properties of raw materials used for the properties include, for example, the following: iron, SiO 2 , Al 2 O 3 , SiO 2 , etc. The properties for preparing a coal blending plan include the following: CSR (strength after hot reaction), DI (coke strength), VM (volatile matter), expansion pressure, and the like. These properties must satisfy the required property constraints. An example of the property model after mixing is shown in (Equation 14) above. In addition, although (Formula 14) shows the example which has the lower limit S, when it has an upper limit, it may have both an upper limit and a lower limit.
　ここで、多くの性状について、性状モデルに含まれる数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）は、上記の（式１５）に示すように、配合割合に対して線形となる。例えば、SiO2に関して、銘柄Ａの配合割合が４０％、銘柄ＡのSiO2成分が１%、銘柄Ｂの配合割合が６０％、SiO2成分が２%の条件で混合した場合、混合後のSiO2成分の性状は、１×０．４＋２×０．６＝１．６％となる。 Here, for many properties, the formula f (xA, xB, xC,..., XN) included in the property model is linear with respect to the blending ratio as shown in (Formula 15) above. For example, with respect to SiO 2, 40% proportion of the stock A is SiO 2 component is 1% stock A, 60% proportion of the stock B is, if the SiO 2 component is mixed with 2% condition, after mixing The property of the SiO 2 component is 1 × 0.4 + 2 × 0.6 = 1.6%.
　ところが、性状によっては、その性状を表す数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）が非線形となることがある。この場合、次に述べるように、線形化部３５７ａで、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に代えて線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）を導入して数式モデルを定式化する。 However, depending on the properties, the mathematical expression f (xA, xB, xC,..., XN) representing the properties may be nonlinear. In this case, as described below, the linearizing unit 357a replaces the nonlinear mathematical expression f (xA, xB, xC,..., XN) with the linear mathematical expression f ′ (xA, xB, xC,. XN) to formulate the mathematical model.
　線形化部３５７ａでの処理について説明する。ある性状を表す数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）が非線形である場合、それに代えて線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）を導入する。この線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）は、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）の下限をなすもの、すなわち上記の（式１６）の関係が成立するものを考える。なお、（式１６）は常に成立する必要はなく、必要な範囲で成立していればよい。 Processing in the linearization unit 357a will be described. When the mathematical expression f (xA, xB, xC,..., XN) representing a certain property is nonlinear, a linear mathematical expression f ′ (xA, xB, xC,..., XN) is introduced instead. This linear formula f ′ (xA, xB, xC,..., XN) forms a lower limit of the nonlinear formula f (xA, xB, xC,..., XN), that is, the above (Formula 16). ) Is considered. Note that (Equation 16) does not always need to be satisfied, and only needs to be satisfied within a necessary range.
　例えば線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）として、上記の（式１７)に示す加重平均を考える。加重平均は、単一銘柄を１００％使用した場合の性状を非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）から求め、配合割合を乗算して、使用銘柄分足し合わせた値である。 For example, the weighted average shown in the above (Expression 17) is considered as a linear mathematical expression f ′ (xA, xB, xC,..., XN). The weighted average is a value obtained by calculating the properties when a single brand is used 100% from a non-linear formula f (xA, xB, xC,..., XN), multiplying the blending ratio, and adding the used brands. It is.
　説明を簡単にするため、銘柄Ａの配合割合が９０％、銘柄Ｃの配合割合が１０％の例を考える。この場合、線形の数式ｆ´（９０、０、１０、・・・、０）となる加重平均は、下式で表される。
　　ｆ´（９０、０、１０、・・・、０）
　　＝０．９×ｆ（１００、０、・・・、０）＋０．１×ｆ（０、０、１００、・・・０） To simplify the explanation, consider an example in which the blending ratio of brand A is 90% and the blending ratio of brand C is 10%. In this case, a weighted average that is a linear mathematical expression f ′ (90, 0, 10,..., 0) is represented by the following expression.
f ′ (90, 0, 10,..., 0)
= 0.9 x f (100, 0, ..., 0) + 0.1 x f (0, 0, 100, ... 0)
　過去の実績等から、この加重平均が（式１６）を満たすことがわかっていれば、この加重平均を線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）として利用することができる。すなわち、加重平均≧Ｓを制約とすれば、（式１４）が成立するものとみなすことで、定式化できる可能性が得られる。 If it is known from the past results that this weighted average satisfies (Expression 16), this weighted average can be used as a linear expression f ′ (xA, xB, xC,..., XN). it can. That is, if weighted average ≧ S is a constraint, it is possible to formulate by assuming that (Equation 14) holds.
　線形化部３５７ａでは、上記の（式１４）´に示すように、線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）に対する下限値として、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に対する下限値Ｓよりも小さな仮下限値Ｓ´＝Ｓ－ｓ（ｓ：オフセット値）を設定して数式モデルを設定する。 In the linearization unit 357a, as shown in the above (Expression 14) ′, as a lower limit value for the linear expression f ′ (xA, xB, xC,..., XN), the nonlinear expression f (xA, xB, A mathematical model is set by setting a temporary lower limit S ′ = S−s (s: offset value) smaller than the lower limit S for xC,.
　以上は、混合後の性状制約が下限値を有する場合を例に説明した。なお、上述した性状制約は一例であり、他の制約に替えたり（混合後の性状制約が上限値を有する場合を含む）、他の制約を加えたりしてもよい。 The above is an example in which the property constraint after mixing has a lower limit. The property constraints described above are merely examples, and other constraints may be substituted (including cases where the property constraint after mixing has an upper limit), or other constraints may be added.
　以上説明した需給バランスモデル設定部３５６（需給バランスモデル設定部３１３）及びステップＳ３０６、並びに、性状モデル設定部３５７（図３の性状モデル設定部３１４）及びステップＳ３０７、Ｓ３０７ａが、本発明でいう第２の配合計画作成部の数式モデル設定部及びそれによる処理例である。 The supply / demand balance model setting unit 356 (supply / demand balance model setting unit 313) and step S306, the property model setting unit 357 (properties model setting unit 314 in FIG. 3), and steps S307 and S307a described above are referred to in the present invention. It is a numerical formula model setting part of 2 combination plan preparation parts, and the example of a process by it.
（８）固定化抽出処理（図４の固定化抽出処理部３５８、図５のステップＳ３０８）
　図９に示すように、配船計画の項目である積港、積銘柄、積量、揚港、揚銘柄、揚量のうち固定化されているもの、すなわち変更できないものを抽出する。予め与えられる条件によって、各傭船に対して「積港」及び「積銘柄」が固定化されている場合は、船舶別・積港別・揚港別フレート（図１５を参照）を用いる。つまり、これらの傭船は原材料を積載する傭船が決定されているため、船舶別・積港別・揚港別フレートを用いることで、最適化によって原材料を入荷する製鉄所が決定された時点で、正確な輸送費用計算が可能となる。 (8) Immobilization extraction processing (immobilization extraction processing unit 358 in FIG. 4, step S308 in FIG. 5)
As shown in FIG. 9, items that are fixed, that is, those that cannot be changed, are extracted from among the shipping port items, loading brands, loadings, landing ports, lifting brands, and lifting amounts, which are items of the ship allocation plan. When the “loading port” and “loading brand” are fixed for each dredger according to the conditions given in advance, the freight rate for each ship, each loading port, and each lifting port (see FIG. 15) is used. In other words, since these dredgers have been determined to be loaded with raw materials, by using the freight by ship, by port, by port, and when the steelworks that will receive the raw materials is determined by optimization, Accurate transportation cost calculation is possible.
　また、いずれの項目も固定化されていない場合や、「積港」だけが固定化されている場合は、銘柄別・揚港別見做しフレートを用いる。つまり、積港と積銘柄が決定されていない場合には、当該船舶に関する積港と積銘柄自体を変更可能にすることで、後述の最適化を用いて、当該積港と積銘柄より輸送費用の安い積港と積銘柄の有無を検討することが可能となるこの場合は、銘柄別・揚港別見做しフレートを用いることで、当該船舶に関して、当該積港と積銘柄より輸送費用の安い積港と積銘柄に、当該船舶の積港と積銘柄を変更させることを後述する最適化により計画させる。これにより輸送費用のより安い計画を作成することを可能としている。なお、同一の傭船に関しては、固定化が最もされていないレコードの状態をこの傭船の固定化状況と考える。この固定化抽出処理は、図５に示したタイミングである必要はなく、例えば配合計画作成を開始するときに行われるようにしてもよい。 Also, if none of the items are fixed, or if only “Sekiko” is fixed, use the freight rate by brand / shipping port. In other words, when the loading port and the brand name have not been determined, the shipping port and the loading name for the ship can be changed, so that the shipping cost can be changed from the loading port and the loading brand using the optimization described later. In this case, it is possible to consider whether there is a low-cost loading port and a brand name. The cheap loading port and the loading brand are planned to change the loading port and loading brand of the ship by the optimization described later. This makes it possible to create a plan with lower transportation costs. For the same dredger, the state of the record that is not fixed most is considered as the anchoring state of this dredger. This immobilization extraction process does not need to be at the timing shown in FIG. 5, and may be performed, for example, when the formulation plan creation is started.
（９）配合計画数式モデルを目的関数に基づいて最適化（図３の計画部３１５、図４の配合計画求解部３５９、図５のステップＳ３０９）
　上記構築された線形及び整数制約式でなる需給バランスモデル、性状モデルを併せて配合計画数式モデルとし、予め構築した目的関数に基づきＬＰ（線形計画法）、ＭＩＰ（混合整数計画法）、ＱＰ（２次計画法）等の数理計画法により最適化問題として問題を解くことにより、最適な使用量、入荷量を計算する。 (9) Optimize formulation formula mathematical model based on objective function (planning unit 315 in FIG. 3, formulation plan solution unit 359 in FIG. 4, step S309 in FIG. 5)
Combined supply and demand balance model and property model composed of linear and integer constraint formulas described above are combined into a blending plan formula model, and LP (Linear Programming), MIP (Mixed Integer Programming), QP ( The optimal usage and arrival are calculated by solving the problem as an optimization problem by mathematical programming such as quadratic programming.
　ここでは、目的関数に関して線形式を用いた場合の例を示す。本実施形態では、費用（原材料の購入費用及び輸送費用）のミニマム化を目的としており、目的関数Ｊの一例を（式４０）に示す。目的関数を用いて求解するに際して、購入費用情報及びステップＳ３０８において設定された輸送費用情報を用いる。 Here, an example of using the linear format for the objective function is shown. In the present embodiment, the objective is to minimize costs (raw material purchase costs and transportation costs), and an example of an objective function J is shown in (Equation 40). When solving using the objective function, the purchase cost information and the transportation cost information set in step S308 are used.
Figure JPOXMLDOC01-appb-M000051
Figure JPOXMLDOC01-appb-M000051
　なお、（式４０）は目的関数の一例であり、他の目的関数に替えたり、他の目的関数を加えたりしてもよい。例えば、与えられた目標とする配合割合に近い配合割合に配合計画を近づける必要があり、更に前日の配合割合とその翌日の配合割合が大きく乖離することがない配合計画を作成する必要がある場合を考える。この場合は、使用目標量からの溢れ量、不足量、及び当該日の使用量と当該日前日の使用量との差をミニマム化する項目を目的関数に追加する。 Note that (Equation 40) is an example of an objective function, and other objective functions may be substituted or other objective functions may be added. For example, when it is necessary to bring the blending plan closer to the blending ratio close to the given target blending ratio, and it is necessary to create a blending plan in which the blending ratio of the previous day and the blending ratio of the next day do not greatly differ think of. In this case, an overflow amount, a deficiency amount from the target usage amount, and items for minimizing a difference between the usage amount on the day and the usage amount on the day before are added to the objective function.
Figure JPOXMLDOC01-appb-M000052
Figure JPOXMLDOC01-appb-M000052
　以上の定式化した式（数式モデル）を展開、設定して、混合整数計画法にて解くことにより、需給バランスモデル、性状モデルを併せた配合計画数式モデルに対する最適解が得られる。 By developing and setting the above formulated formula (formula model) and solving it by the mixed integer programming method, an optimal solution for the blended plan formula model combining the supply and demand balance model and the property model can be obtained.
　以上説明した配合計画求解部３５９（計画部３１５）及びステップＳ３０９が、本発明でいう第２の配合計画作成部の最適化計算部及びそれによる処理の例である。 The above-described blending plan solution unit 359 (planning unit 315) and step S309 are examples of the optimization calculation unit of the second blending plan creation unit referred to in the present invention and processing by it.
（１０）最適化計算による求解結果の判定（図４の求解結果判定部３６０、図５のステップＳ３１０、Ｓ３１１）
　（式１４）´を用いた最適化計算による求解結果が、非線形の数式を含む数式モデルｆ（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓを満たすか否かを判定する。その結果、非線形の数式を含む数式モデルｆ（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓを満たせば、この求解結果に従い、後述する性状シミュレータ３６２に対する計算指示を作成して、シミュレーションを実行させる。非線形の数式を含む数式モデルｆ（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓを満たさなければ、線形の数式を含む数式モデルｆ´（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓ´を調整する（図５のステップＳ３１１）。具体的には、仮下限値Ｓ´を微増させる。 (10) Determination of solution result by optimization calculation (solution result determination unit 360 in FIG. 4, steps S310 and S311 in FIG. 5)
It is determined whether or not the solution obtained by the optimization calculation using (Expression 14) ′ satisfies a mathematical model f (xA, xB, xC,..., XN) ≧ S including a nonlinear mathematical expression. As a result, if a mathematical model f (xA, xB, xC,..., XN) ≧ S including a nonlinear mathematical formula is satisfied, a calculation instruction for a property simulator 362 described later is created according to the solution result, and a simulation is performed. Let it run. Formula model f ′ (xA, xB, xC,..., XN) including a linear formula if the formula model f (xA, xB, xC,..., XN) ≧ S including a nonlinear formula is not satisfied. ≧ S ′ is adjusted (step S311 in FIG. 5). Specifically, the temporary lower limit S ′ is slightly increased.
　図１０は、ステップＳ３０７～Ｓ３１０の処理、すなわち非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に代えて線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）を導入したときの処理を示すフローチャートである。ステップＳ４０１において、需給バランスモデル、性状モデル（非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に代えて線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）を導入して構築したもの）、目的関数Jに基づいて最適化計算を実行する。 FIG. 10 shows the processing in steps S307 to S310, that is, the linear mathematical expression f ′ (xA, xB, xC,..., XN) instead of the nonlinear mathematical expression f (xA, xB, xC,..., XN). It is a flowchart which shows a process when introduce | transducing. In step S401, a linear equation f ′ (xA, xB, xC,..., XN) is used in place of the demand-supply balance model and the property model (nonlinear equations f (xA, xB, xC,..., XN)). Optimized calculation based on the objective function J).
　この場合に、（式１４）´に示したように、線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）に対する下限値として、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に対する下限値Ｓよりも小さな仮下限値Ｓ´＝Ｓ－ｓ（ｓ：オフセット値）を設定する。 In this case, as shown in (Expression 14) ′, as a lower limit value for the linear expression f ′ (xA, xB, xC,..., XN), the nonlinear expression f (xA, xB, xC,. A temporary lower limit value S ′ = S−s (s: offset value) smaller than the lower limit value S for xN) is set.
　次にステップＳ４０２において、線形の数式を含む数式モデルｆ´（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓ´を用いた最適化計算による求解結果が、非線形の数式を含む数式モデルｆ（ｘA、ｘB、ｘC、・・・、ｘN）≧Ｓを満たすか否かを判定する。すなわち、ステップＳ４０１の最適化計算による求解結果（各銘柄Ａ～Ｎの使用量（配合割合））を（式１４）に代入し、（式１４）が成立するか否かを判定する。 Next, in step S402, the solution obtained by the optimization calculation using the mathematical model f ′ (xA, xB, xC,..., XN) ≧ S ′ including a linear mathematical expression is a mathematical model f including a nonlinear mathematical expression. It is determined whether (xA, xB, xC,..., XN) ≧ S is satisfied. That is, the result of the optimization calculation in step S401 (the usage amount (mixing ratio) of each brand A to N) is substituted into (Expression 14), and it is determined whether (Expression 14) is satisfied.
　ステップＳ４０２の結果、（式１４）が成立すれば、本処理を終了する（図１０のステップＳ４１２に移行する）。それに対して、（式１４）が成立しなければ、ステップＳ４０３に進んで、仮下限値Ｓ´を予め設定された増減幅で微増させて、再度ステップＳ４０１の処理を実行する。すなわち、（式１４）が成立するまで、仮下限値Ｓ´を微増させて、最適化計算による求解を繰り返す収束計算を実行する。 As a result of Step S402, if (Equation 14) is established, this process is terminated (the process proceeds to Step S412 in FIG. 10). On the other hand, if (Equation 14) does not hold, the process proceeds to step S403, where the temporary lower limit S ′ is slightly increased by a preset increase / decrease range, and the process of step S401 is executed again. That is, the convergence calculation is repeated until the provisional lower limit S ′ is slightly increased and the solution by the optimization calculation is repeated until (Equation 14) is satisfied.
　なお、本実施形態では、混合後の性状制約が下限値を有する場合を例にして説明したが、上限値を有する場合も同様である。この場合、線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）は、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）の上限をなすものを考える。また、ステップＳ４０１では、線形の数式ｆ´（ｘA、ｘB、ｘC、・・・、ｘN）に対する上限値として、非線形の数式ｆ（ｘA、ｘB、ｘC、・・・、ｘN）に対する上限値よりも大きな仮上限値を設定する。 In the present embodiment, the case where the property constraint after mixing has a lower limit value has been described as an example, but the same applies to the case where the property constraint has an upper limit value. In this case, the linear formula f ′ (xA, xB, xC,..., XN) is considered to form an upper limit of the nonlinear formula f (xA, xB, xC,..., XN). In step S401, the upper limit value for the nonlinear mathematical expression f (xA, xB, xC,..., XN) is set as the upper limit value for the linear mathematical expression f ′ (xA, xB, xC,..., XN). Set a larger temporary upper limit.
（１１）求解した解に基づいて在庫推移をシミュレーション（図３の在庫推移シミュレータ３１１、図４の在庫推移シミュレータ３６１、図５のステップＳ３１２）
　上記配合計画数式モデルに対する解、及び、入力データ取込み部３５１により取込まれたデータの全部又は一部に基づいて、配合の全部或いは一部を対象として、設定した計画確定期間分について、設定した計画作成精度でシミュレーションを実行する。このシミュレーションでは、配合計画数式モデルには組込むことができなかった制約条件、例えば一定の規則に基づかない条件など、定式化が難しいもの、及び、操業のルール等も組み込んでシミュレートする。このことで、配合計画数式モデルに対する求解結果として出された解を実操業で問題なく使用可能な配合計画に変更する。これにより、実操業で求められる時間精度と、実操業に求められる細かな制約まで考慮した配合計画の立案が可能となる。 (11) Simulation of inventory transition based on the obtained solution (inventory transition simulator 311 in FIG. 3, inventory transition simulator 361 in FIG. 4, step S312 in FIG. 5)
Based on the solution to the above blending plan mathematical formula model and the whole or part of the data fetched by the input data fetching unit 351, it was set for the set plan confirmation period for all or part of the blending. Run the simulation with planning accuracy. In this simulation, a simulation is performed by incorporating restrictions that could not be incorporated into the formulation planning mathematical model, such as conditions that are difficult to formulate, such as conditions that are not based on certain rules, and operation rules. As a result, the solution obtained as a solution to the blending plan mathematical model is changed to a blending plan that can be used without problems in actual operation. This makes it possible to formulate a blending plan that takes into account the time accuracy required in actual operation and the fine restrictions required in actual operation.
　また、数式モデルでは取扱うことが難しい制約の一例として、配合割合が変わった場合の設備の段取りに掛かる段取時間等をシミュレーションに取込み、正確にシミュレートすることで、実操業に求められる細かな制約まで考慮した配合計画の立案が可能となる。 In addition, as an example of a constraint that is difficult to handle with a mathematical model, the time required for equipment setup when the mixing ratio changes is taken into the simulation, and the detailed simulation required for actual operation is achieved by accurately simulating it. It is possible to create a blending plan that takes into account the constraints.
（１２）求解した解に基づいて性状をシミュレーション（図３の性状シミュレータ３１２、図４の性状シミュレータ３６２、図５のステップＳ３１３）
　上記配合計画数式モデルに対する解、在庫推移シミュレータ３６１によりシミュレーションされた在庫推移、及び、入力データ取込み部３５１により取込まれたデータの全部或いは一部に基づいて、配合の全部或いは一部を対象として、設定した計画確定期間分について、設定した計画作成精度で、性状のシミュレートをおこなう。シミュレーションの結果として、原材料の混合後の性状結果が得られる。このシミュレーションでは、配合計画数式モデルには組み込むことができなかった制約条件、操業のルール等も組み込んでシミュレートすることで、配合計画数式モデルに対する求解結果として出された解を実操業で問題なく使用可能な配合計画に変更する。これにより、実操業で求められる時間精度と、実操業に求められる細かな制約まで考慮した配合計画の立案が可能となる。 (12) Simulate the properties based on the solved solution (the property simulator 312 in FIG. 3, the property simulator 362 in FIG. 4, and step S313 in FIG. 5)
Based on the solution to the above-mentioned formula planning formula model, the inventory transition simulated by the inventory transition simulator 361, and all or part of the data captured by the input data capturing unit 351, all or part of the composition is targeted The property is simulated for the set plan finalization period with the set plan creation accuracy. As a result of the simulation, a property result after mixing the raw materials is obtained. In this simulation, by incorporating simulation conditions including constraints, operation rules, etc. that could not be incorporated into the formulation planning formula model, the solution obtained as a solution result for the formulation planning formula model can be used without problems in actual operation. Change to a usable recipe. This makes it possible to formulate a blending plan that takes into account the time accuracy required in actual operation and the fine restrictions required in actual operation.
　以上説明した在庫推移シミュレータ３６１（在庫推移シミュレータ３１１）及びステップＳ３１２、並びに、性状シミュレータ３６２（性状シミュレータ３１２）及びステップＳ３１３が、本発明でいう第２の配合計画作成部のシミュレータ及びそれによる処理の例である。 The inventory transition simulator 361 (inventory transition simulator 311) and step S312, and the property simulator 362 (property simulator 312) and step S313 described above are the simulator of the second blending plan creation unit referred to in the present invention and the processing performed thereby. It is an example.
（１３）配合計画の確定（図４の確定部３６３、図５のステップＳ３１４）
　上記在庫推移シミュレーション、性状シミュレーションにより導き出された配合計画のうちで設定した計画確定期間分を確定する。図７に示すように、本実施形態では計画確定期間を１日と設定しているので、作成した配合計画の最初の１日分を確定する。作成した配合計画のうちで上記計画確定期間に入らなかった部分については、その計画は確定せずに破棄する。 (13) Confirmation of blending plan (confirmation unit 363 in FIG. 4, step S314 in FIG. 5)
The plan decision period set in the combination plan derived by the inventory transition simulation and the property simulation is confirmed. As shown in FIG. 7, in this embodiment, since the plan determination period is set to one day, the first one day of the created formulation plan is fixed. Of the created blending plan, the portion that has not entered the plan finalization period is discarded without being finalized.
（１４）計画作成期間分、或いは計画確定期間分の計画が確定したか判定（図４の判定部３６４、図５のステップＳ３１５）
　このステップの実行時点までに確定した計画確定期間が予め設定した計画作成期間の全体を含んでいるかを判断する。本実施形態では、計画作成期間が１０日間であるので、第１０ループで計画を確定した時点で計画確定期間分の計画が確定する。このため第１０ループで計画を確定終了した時点で１０日分の配合計画を作成して、処理を終了する。 (14) Judgment whether the plan for the plan creation period or the plan for the plan confirmation period has been confirmed (determination unit 364 in FIG. 4, step S315 in FIG. 5)
It is determined whether the plan finalization period determined up to the execution time of this step includes the entire preset plan creation period. In this embodiment, since the plan creation period is 10 days, the plan for the plan confirmation period is confirmed when the plan is confirmed in the tenth loop. For this reason, when the plan is finalized in the tenth loop, a blending plan for 10 days is created and the process is terminated.
（１５）立案開始日の更新（図４の更新部３６５、図５のステップＳ３１６）
　このステップの実行時点で確定した計画確定期間が予め設定した計画作成期間の全体を含んでいない場合、上記配合計画のうちで確定した配合計画期間直後の日時を新たな立案開始日として設定する。本実施形態では、図７に示すように、第１ループでは当初１日目０時であった立案開始日を２日目０時に、第２ループでは当初２日目０時であった立案開始日を３日目０時に更新する。 (15) Planning start date update (update unit 365 in FIG. 4, step S316 in FIG. 5)
If the plan decision period determined at the time of execution of this step does not include the entire plan preparation period set in advance, the date and time immediately after the combination plan period determined in the combination plan is set as a new planning start date. In the present embodiment, as shown in FIG. 7, the planning start date that was initially 0 o'clock on the first day in the first loop is 0 o'clock on the second day, and the planning start that was originally 0 o'clock on the second day in the second loop is started. Update the day to 0:00 on the third day.
（１６）配合計画の出力（図４の出力部３６６、図５のステップＳ３１７）
　以上のようにして作成した配合計画は、出力部３６６により、表示部３０３に画面表示されたり、データベース４００を含む外部機器にデータ送信されたりする。 (16) Output of formulation plan (output unit 366 in FIG. 4, step S317 in FIG. 5)
The formulation plan created as described above is displayed on the screen of the display unit 303 by the output unit 366 or is transmitted to an external device including the database 400.
　以上説明した出力部３６６及びステップＳ３１７が、本発明でいう第２の配合計画作成部の出力部及びそれによる処理の例である。 The output unit 366 and step S317 described above are examples of the output unit of the second blending plan creation unit and the processing performed thereby in the present invention.
　以上のように、現在の在庫推移状態に応じて、需給バランス制約、性状制約について、まず所定の最適化期間分について、計画作成時間精度で数式モデルを設定し、設定した配合計画数式モデルを目的関数に基づいて求解し、求解した解に基づいて、在庫推移、混合後の性状をシミュレートし、シミュレーション結果から求められた配合計画のうちで、設定した計画確定期間分を確定し、計画確定期間直後の日時を新たな立案開始日時とすることにより、新たな計画対象期間分の配合計画を確定する一連の処理を順次、予め定めた回数だけ、繰り返して実行する。このことで、所望する計画作成期間分の配合計画を作成することができる。これにより、任意の時間精度を必要とする配合計画を高速かつ詳細に最適化することができ、しかも得られた結果をそのままで実操業に適用できる。 As described above, according to the current inventory transition status, for the supply and demand balance constraint and property constraint, first, a mathematical model is set with the plan creation time accuracy for the predetermined optimization period, Solves based on the function, simulates inventory transition and mixed properties based on the solved solution, confirms the set plan finalization period from the formulation plan obtained from the simulation results, and finalizes the plan By setting the date and time immediately after the period as a new planning start date and time, a series of processes for determining a blending plan for a new planning target period is sequentially and repeatedly executed a predetermined number of times. Thereby, the mixing | blending plan for the plan preparation period desired can be created. This makes it possible to optimize a blending plan that requires an arbitrary time accuracy at high speed and in detail, and to apply the obtained results to actual operations as they are.
　なお、上記実施形態では、図１１に示すように、配合計画は一定の期間（例えば旬）毎に作成される。また、複数の性状α、βについて性状モデルが非線形となることがある。図１１において、Ａは性状制約を満たしている（（式１４）が成立している）ことを、Ｂは性状制約を満たしていないことを意味する。すなわち、図１１の例では、性状αについて複数旬（４月上旬及び下旬）で性状違反が発生しており、同様に性状βについて複数旬（４月上旬及び下旬）で性状違反が発生している。 In the above embodiment, as shown in FIG. 11, the blending plan is created every certain period (for example, seasonal). In addition, the property model may be nonlinear with respect to a plurality of properties α and β. In FIG. 11, A means that the property constraint is satisfied (Equation 14 is satisfied), and B means that the property constraint is not satisfied. In other words, in the example of FIG. 11, there are property violations occurring in multiple seasons (early and late April) for property α, and similarly, property violations occur in multiple seasons (early and late April) for property β. Yes.
　この場合に、各旬及び各性状について別個に図１０で説明した収束計算を行うと、以下の問題が生ずる。具体的にいえば、４月上旬で性状αについて収束計算を行い、続いて性状βについて収束計算を行い、また、４月下旬で性状αについて収束計算を行い、続いて性状βについて収束計算を行うのでは、計算処理に時間がかかってしまう。 In this case, if the convergence calculation described in FIG. 10 is performed separately for each season and each property, the following problems occur. Specifically, the convergence calculation is performed for the property α in early April, the convergence calculation is performed for the property β, the convergence calculation is performed for the property α in late April, and the convergence calculation is performed for the property β. Doing so will take time for the calculation process.
　そこで、第１の実施形態でも説明したように、対象の旬及び性状についてまとめて図１０で説明した収束計算を行うようにする。例えば４月上旬及び下旬で性状α、βについてまとめて収束計算を行う（図１０のステップＳ４０３で性状α、βの仮下限値の微増（或いは仮上限値の微減）を同時に行う）ことにより、高速化を図ることができる。 Therefore, as described in the first embodiment, the convergence calculation described in FIG. For example, by performing convergence calculation for the properties α and β at the beginning and the end of April (by slightly increasing the temporary lower limit values (or slightly decreasing the temporary upper limit value at the same time in step S403 in FIG. 10)), The speed can be increased.
　また、上記実施形態では、図１０のステップＳ４０３で仮下限値Ｓ´を微増（或いは仮上限値を微減）させた後、再度ステップＳ４０１の処理を実行すると説明した。この場合に、収束計算が変化しても設定に変化のない数式モデル、具体的には上述した需給バランスモデルや元々線形の性状モデルは保持しておく。そして、仮下限値を微増（或いは仮上限値を微減）させて再度ステップＳ４０１の処理を実行する場合に、収束計算に従って変化のある数式モデル、具体的には仮下限値を微増させた（或いは仮上限値を微減させた）数式モデルのみ変更するような仕組とすることにより、高速化を図ることができる。 In the above-described embodiment, it has been described that the temporary lower limit value S ′ is slightly increased (or the temporary upper limit value is slightly decreased) in step S403 in FIG. In this case, a mathematical model in which the setting does not change even when the convergence calculation changes, specifically, the above-described supply-demand balance model or an originally linear property model is retained. Then, when the provisional lower limit value is slightly increased (or the provisional upper limit value is slightly decreased) and the process of step S401 is executed again, the mathematical model that changes according to the convergence calculation, specifically, the provisional lower limit value is slightly increased (or Speeding up can be achieved by adopting a structure in which only the mathematical model (with the temporary upper limit value slightly reduced) is changed.
　また、配合計画（例えば使用量（配合割合））として、年次計画、期計画、月次計画といった長期間の計画を立案することが多い。このように長期の配合計画を予め作成し、その配合計画を基準の配合計画とし、第１の配合計画作成装置１００により作成されたより短期の配合計画が、基準となる配合計画から一定幅以上離れないようにすることも重要となる。 Also, in many cases, a long-term plan such as an annual plan, a term plan, or a monthly plan is formulated as a blending plan (for example, usage (mixing ratio)). In this way, a long-term blending plan is created in advance, the blending plan is used as a reference blending plan, and the shorter-term blending plan created by the first blending plan creation device 100 is more than a certain distance away from the reference blending plan. It is also important to avoid it.
　そこで、（式１７）に示したような費用（原材料の購入費用及び輸送費用）に関して構築された目的関数Jに加え、予め作成された基準となる配合計画と一定幅以上離れないようにすることに関して構築された目的関数J´を用いるようにしてもよい。目的関数Ｊ´の一例を（式２０）に示す。 Therefore, in addition to the objective function J constructed with respect to the costs shown in (Equation 17) (raw material purchase costs and transportation costs), keep it within a certain range from the preliminarily created blending plan. The objective function J ′ constructed for may be used. An example of the objective function J ′ is shown in (Equation 20).
　上記例では、月次計画において、期計画を基準となる配合計画として、日々の配合計画を作成する場合の一例を示した。この場合は、配合割合（銘柄、日）と基準配合割との差の銘柄毎、日毎に合計したものをミニマム化する。他の例として、期計画を立案する場合、年次計画を基準となる配合計画として計画を作成しても良い。この場合、月次計画では配合割合（銘柄、月）を決定するとした場合は、配合割合（銘柄、月）と基準配合割との差を銘柄毎、月毎に合計した値をミニマム化する。 In the above example, an example of creating a daily blending plan as a blending plan based on the term plan in the monthly plan is shown. In this case, the sum totaled for each brand and each day of the difference between the blending ratio (brand, day) and the standard blending ratio is minimized. As another example, when planning a term plan, the plan may be created as a blending plan based on the annual plan. In this case, in the monthly plan, when it is determined that the blending ratio (brand, month) is determined, the difference between the blending ratio (brand, month) and the standard blending ratio is summed for each brand and every month.
　なお、基準となる配合計画は、例えば過去の実績に基づいて作成され、その作成手法はどのようなものであってもよい。もちろん、本発明を適用した配合計画作成手法により長期間の計画を予め作成しておき、それを基準となる配合計画としてもよい。 In addition, the mixing | blending plan used as a reference | standard is produced based on the past performance, for example, The production method may be what kind. Of course, a long-term plan may be created in advance by a blending plan creation method to which the present invention is applied, and this may be used as a reference blending plan.
　以上により、複数銘柄の配合原材料を入荷して混合する配合計画、及び複数銘柄の原材料を複数の積地から複数の揚地に輸送する配船計画を相互に連係させて一括で作成することが可能となる。また、これら計画においては、配合計画・配船計画を通して全体として輸送費用をミニマム化することが可能となる。更に、第１の配合計画作成装置１００により使用計画を作成し、この使用計画を入力データとして配船計画を作成する場合より、より確実に在庫切れを防ぐことができる配合計画・配船計画を作成できる。 As described above, a blending plan for receiving and mixing multiple brands of raw materials and a ship allocation plan for transporting multiple brands of raw materials from multiple loading points to multiple landing sites can be created in a batch. It becomes possible. Moreover, in these plans, it becomes possible to minimize the transportation cost as a whole through the blending plan / ship allocation plan. Furthermore, a blending plan / shipping plan that can prevent out-of-stock more reliably than when creating a use plan with the first blending plan creation device 100 and creating a ship assignment plan using this use plan as input data is provided. Can be created.
（第３の実施形態）
　上記第２の実施形態では、配船計画作成装置２００において、各銘柄の在庫に関する制約（式２４）として、個別の銘柄毎に安全在庫量を切らないことがある。しかし、実際の操業においては、性状が近い銘柄（規定した範囲に含まれる化学性質を共通して備える複数の銘柄：互いに置き換えても使用可能な銘柄）は、互いに置き換えて使用することで、融通を利かせている。例えば、原材料Ａ，Ｂの在庫量がそれぞれ５万トン、０トンの場合、使用計画において原材料Ａが０トン、Ｂが２万トンの場合、Ｂ銘柄のみ考慮すれば在庫切れが発生する。しかし、原材料Ａ，Ｂの性状が、互いに置き換えても使用可能な関係である場合は、原材料Ｂを２万トン使用する代わりにＡを２万トン使用する運用が行われている。こうすることで、在庫切れを防止できる。
　例えば石炭の場合、「石炭化度が７０％以下；７０％より大きく９０％以下；９０％より大きい」等の、原材料の物理的、または化学的な性状に関する条件を用いて、銘柄のグループ化を行う。　また、このように銘柄をグループ化し、一つのものとして取り扱うことにより、変数を少なくして計算量を減らすことができる。 (Third embodiment)
In the second embodiment, in the ship allocation plan creating apparatus 200, the safety stock amount may not be cut for each individual brand as a constraint on the stock of each brand (Equation 24). However, in actual operations, brands with similar properties (multiple brands that share the same chemical properties within the specified range: brands that can be used even if they are replaced with each other) can be used interchangeably. To make use of. For example, if the stock quantities of raw materials A and B are 50,000 tons and 0 tons, respectively, if the raw material A is 0 tons and B is 20,000 tons in the usage plan, out of stock will occur if only the B brand is considered. However, when the properties of the raw materials A and B are in a relationship that can be used even if they are replaced with each other, the operation of using 20,000 tons of A instead of using 20,000 tons of the raw material B is performed. This can prevent out of stock.
For example, in the case of coal, grouping of brands using conditions related to physical or chemical properties of raw materials, such as “the degree of coalification is 70% or less; greater than 70% and less than 90%; greater than 90%”. I do. In addition, grouping the brands in this way and treating them as one makes it possible to reduce the number of variables and the amount of calculation.
　本実施形態においては、上記運用を実現する配合計画・配船計画の作成を実現することを目的とする。このため、配船計画作成装置２００において、各銘柄の在庫に関する制約を個別の銘柄毎に安全在庫量を切らないものとして取り扱うのではなく、相互に代替可能な性状を持つ銘柄は、グループ化して取り扱う。つまり、グループ化された銘柄においては、グループとしての銘柄の在庫量が、グループとしての安全在庫量を切らないものとして、在庫制約を作成する。 In this embodiment, an object is to realize creation of a blending plan / ship allocation plan that realizes the above operation. For this reason, the ship allocation plan creation device 200 does not treat the restrictions on the stock of each brand as if the safety stock amount is not cut for each individual brand, but groups brands having properties that can be substituted for each other. handle. That is, in the grouped brands, the stock constraint is created on the assumption that the stock quantity of the brand as a group does not cut the safety stock quantity as the group.
　つまり、配船計画作成装置２００において、（式２４）の代わりに、各グループ化された銘柄の在庫量が常にグループとしての安全在庫以上確保されていることを表す制約式を用いる。この制約式は、下記の（式４２）と表される。 That is, in the ship allocation plan creation device 200, instead of (Equation 24), a constraint equation that represents that the stock amount of each grouped brand is always more than the safety stock as a group is used. This constraint equation is expressed as (Equation 42) below.
Figure JPOXMLDOC01-appb-M000053
Figure JPOXMLDOC01-appb-M000053
　このように銘柄をグループ化して取扱うことで、フレートが高い船でしか輸送できない銘柄に変わり、同一銘柄でフレートのより安い船で手配できる銘柄を輸送することが可能となり、輸送費用を抑制できる。例えば、性状がほぼ同一の原材料Ｘ，Ｙがあり、揚港（製鉄所）Ａでは原材料Ｘ，Ｙどちらでの使用も可能な場合を考える。揚港Ａに原材料Ｘを輸送する費用が２０＄／トン、原材料Ｙを輸送する費用４０＄／トン、揚港Ｂに原材料Ｘを輸送する費用が４０＄／トン、原材料Ｙを輸送する費用２０＄／トン、初期在庫量は全て０、他製鉄所との引取の関係等何らかの理由により、揚港Ａで原材料Ｘの使用が０トン、Ｙの使用が５万トン、揚港Ｂで原材料Ｘの使用が５万トン、Ｙの使用が０トンの配合計画が第１の配合計画作成装置１００により作成された場合を考える。この場合、配船計画において個別に銘柄の在庫を維持する場合には、揚港Ａに原材料Ｙを５万トン、揚港Ｂに原材料Ｘを５万トン入荷する配船が、在庫制約より導かれる。この場合は、４０＄／トン×５万トン×２の輸送費用が必要となる。しかし、代替可能な性状を持つ銘柄の群をグループとして考える場合には、揚港Ａに原材料Ｘを５万トン、揚港Ｂに原材料Ｙを５万トン入荷する計画を配船計画で立てた場合も、揚港Ａ，Ｂ共にＸとＹの合計の在庫量が０を下回らないこととなり、在庫制約を満たす。この場合の輸送費用は、２０＄／トン×５万トン×２となり、グループを考慮しない場合に比べて、半分の費用となる。 こ の By handling brands in groups in this way, it becomes possible to transport brands that can be transported by cheaper freight ships with the same brand, instead of brands that can only be transported by high-freight ships, thereby reducing transportation costs. For example, let us consider a case in which there are raw materials X and Y having substantially the same properties, and the use of raw materials X and Y is possible at Yanggang (ironworks) A. The cost of transporting the raw material X to the unloading port A is 20 $ / ton, the cost of transporting the raw material Y is 40 $ / ton, the cost of transporting the raw material X to the unloading port B is 40 $ / ton, and the cost of transporting the raw material Y is 20 $ / Ton, initial inventory is all 0, for some reason such as taking over with other steelworks, raw material X is used at unloading port A at 0 ton, Y is used at 50,000 ton, raw material X at unloading port B Consider a case where a blending plan in which the use of 50,000 is used and the use of Y is 0 ton is created by the first blending plan creation device 100. In this case, in order to maintain the stock of the brands individually in the ship allocation plan, the allocation of 50,000 tons of raw material Y to the unloading port A and 50,000 tons of the raw material X to the unloading port B leads from the inventory constraints. It is burned. In this case, transportation costs of 40 $ / ton × 50,000 tons × 2 are required. However, when considering a group of brands with substitutable properties as a group, a plan to ship 50,000 tons of raw material X to Yacht A and 50,000 tons of raw material Y to Yacht B was made in the ship assignment plan. In this case, the total inventory amount of X and Y does not fall below 0 for both of the unloading ports A and B, and the inventory constraint is satisfied. In this case, the transportation cost is 20 $ / ton × 50,000 tons × 2, which is half the cost compared to the case where the group is not considered.
　以上により、複数銘柄の配合原材料を入荷して混合する配合計画、及び複数銘柄の原材料を複数の積地から複数の揚地に輸送する配船計画を相互に連係させて一括で作成することが可能となる。また、これら計画においては、配合計画・配船計画を通して全体として輸送費用をミニマム化することが可能となる。更に、第１の配合計画作成装置１００により使用計画を作成し、この使用計画に関する入力データとして、代替可能な性状を持つ銘柄をグループとして考えることにより、在庫を切らさない配船計画を作成する場合と比較して、より輸送費用が安い配合計画・配船計画を作成することが可能となる。 As described above, a blending plan for receiving and mixing multiple brands of raw materials and a ship allocation plan for transporting multiple brands of raw materials from multiple loading points to multiple landing sites can be created in a batch. It becomes possible. Moreover, in these plans, it becomes possible to minimize the transportation cost as a whole through the blending plan / ship allocation plan. Further, when a use plan is created by the first formulation plan creation device 100 and a brand having a property that can be substituted is considered as a group as input data relating to this use plan, a ship allocation plan that does not run out of stock is created. Compared to the above, it is possible to create a blending plan / ship allocation plan with lower transportation costs.
（第４の実施形態）
　第３の実施形態において、第１の配合計画作成装置１００により使用計画を作成し、この使用計画を入力データとして、代替可能な性状を持つ銘柄をグループとして考え、在庫を切らさない配船計画を作成する実施形態を説明した。 (Fourth embodiment)
In the third embodiment, a use plan is created by the first blend plan creation device 100, and the use plan is considered as input data, a brand having an alternative property is considered as a group, and a ship allocation plan that does not run out of stock is created. The embodiment to be created has been described.
　しかし、実際の操業を行う上では、最終的な配合計画・配船計画としては、個別銘柄毎の在庫が確保されていることが必要となる場合が多い。つまり、上記第３の実施形態において、グループ間での入替を行った場合には、第１の配合計画作成装置１００により作成された使用計画と、配船計画作成装置により作成された配船計画では、代替可能な銘柄グループとしての在庫切れは発生しない。しかし、個別の銘柄としては、銘柄間の使用と入荷にずれが生じるため、個別銘柄で在庫を見た場合に在庫が切れているように見え、操業者が扱い難いという問題が発生する。この問題を解決するための実施形態として、配船計画作成装置２００により作成された配船計画を入力データとして、第２の配合計画作成装置３００により、配合計画を作成することで、入荷に沿った配合計画を作成することが可能となる。この場合、代替可能な性状を持つ銘柄間での入替が行われているため、第２の配合計画作成装置３００においても、性状制約に無理なく、配合計画を作成することが可能となる。更に、第１の配合計画作成装置１００では、船舶が決定していない状態で、引取目標量を基に、使用計画を作成しているため、使用計画としては精度が良くない。しかし、第２の配合計画作成装置３００では、配船計画作成装置により船舶が決定した状態、つまり入荷が決定した状態で使用計画を作成するため、より精度が良い使用計画が作成できる。 However, in actual operation, it is often necessary to ensure the stock for each individual brand as the final formulation plan / ship allocation plan. That is, in the third embodiment, when replacement is performed between groups, the use plan created by the first combination plan creation device 100 and the ship assignment plan created by the ship assignment plan creation device. Therefore, stocks that can be replaced as stock groups do not occur. However, since there is a difference in use and receipt between individual brands as individual brands, when the stock is viewed with individual brands, the stock appears to be out of stock, which causes a problem that the operator is difficult to handle. As an embodiment for solving this problem, the shipping plan created by the shipping plan creation device 200 is used as input data, and a blending plan is created by the second blending plan creation device 300. It is possible to create a blending plan. In this case, since replacement between brands having properties that can be substituted is performed, the second blending plan creation device 300 can also create a blending plan without difficulty in property restrictions. Furthermore, in the 1st mixing | blending plan preparation apparatus 100, since the use plan is produced based on the taking over target quantity in the state which the ship has not determined, the accuracy is not good as a use plan. However, in the second blending plan creation apparatus 300, the use plan is created in a state where the ship is determined by the ship allocation plan creation apparatus, that is, the arrival is determined, so that a use plan with higher accuracy can be created.
　以上の構成または方法により、複数銘柄の配合原材料を入荷して混合する配合計画、及び複数銘柄の原材料を複数の積地から複数の揚地に輸送する配船計画を相互に連係させて一括で作成することが可能となる。また、これら計画においては、配合計画・配船計画を通して全体として輸送費用をミニマム化することが可能となる。また、第１の配合計画作成装置１００により使用計画を作成し、この使用計画を入力データとして配船計画を作成する場合と比較して、より輸送費用が安い配合計画・配船計画を作成することが可能となる。さらに、第２の配合計画作成装置３００により使用計画を作成することで、より精度の良い使用計画を作成することが可能となる。 With the above configuration or method, a combination plan that receives and mixes multiple brands of raw materials and a ship allocation plan that transports multiple brands of raw materials from multiple loading sites to multiple landing sites are linked together. It becomes possible to create. Moreover, in these plans, it becomes possible to minimize the transportation cost as a whole through the blending plan / ship allocation plan. Further, a use plan is created by the first blending plan creation device 100, and a blending plan / shipping plan with a lower transportation cost is created as compared with the case where a shipping plan is created using this use plan as input data. It becomes possible. Furthermore, it becomes possible to create a more accurate use plan by creating a use plan by the second blending plan creation device 300.
　図２７には、本発明の配合計画装置１００、３００或いは配船計画作成装置２００として機能しうるコンピュータ装置２５００のハードウェア構成例を示す。装置全体を制御する中央処理装置であるＣＰＵ２５０１、各種入力条件や結果等を表示する表示部２５０２、結果等を保存するハードディスク等の記憶部２５０３、制御プログラム、各種アプリケーションプログラム、データ等を記憶するＲＯＭ（リードオンリーメモリ）２５０４、ＣＰＵ２５０１が処理を行うときに用いる作業領域であるＲＡＭ（ランダムアクセスメモリ）２５０５、及びキーボード、マウス等の入力部２５０６等により構成される。 FIG. 27 shows a hardware configuration example of a computer apparatus 2500 that can function as the blending planning apparatus 100 or 300 or the ship allocation planning apparatus 200 of the present invention. A CPU 2501 that is a central processing unit that controls the entire apparatus, a display unit 2502 that displays various input conditions and results, a storage unit 2503 such as a hard disk that stores results, and a ROM that stores control programs, various application programs, data, and the like (Read Only Memory) 2504, RAM (Random Access Memory) 2505 which is a work area used when the CPU 2501 performs processing, an input unit 2506 such as a keyboard and a mouse, and the like.
　また、上述した実施形態の機能を実現するべく各種のデバイスを動作させるように、この各種デバイスと接続された装置或いはシステム内のコンピュータに対し、上記実施形態の機能を実現するためのソフトウェアのプログラムコードを供給し、そのシステム或いは装置のコンピュータ（ＣＰＵ或いはＭＰＵ）に格納されたプログラムに従って上記各種デバイスを動作させることによって実施したものも、本発明の範疇に含まれる。この場合、上記ソフトウェアのプログラムコード自体が上述した実施形態の機能を実現することになり、そのプログラムコード自体、及びそのプログラムコードをコンピュータに供給するための手段、例えば、かかるプログラムコードを格納した記録媒体は本発明を構成する。プログラムコードを記憶する記録媒体としては、例えばフレキシブルディスク、ハードディスク、光ディスク、光磁気ディスク、ＣＤ－ＲＯＭ、磁気テープ、不揮発性のメモリカード、ＲＯＭ等を用いることができる。 In addition, a software program for realizing the functions of the above-described embodiments for an apparatus or a computer in the system connected to the various devices so that the various devices are operated to realize the functions of the above-described embodiments. What was implemented by supplying the code and operating the various devices in accordance with a program stored in a computer (CPU or MPU) of the system or apparatus is also included in the scope of the present invention. In this case, the program code of the software itself realizes the functions of the above-described embodiment, and the program code itself and means for supplying the program code to the computer, for example, a record storing the program code The medium constitutes the present invention. As a recording medium for storing the program code, for example, a flexible disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used.
　本発明は、以下の態様を更に含む。
（１）　複数の供給元から船舶にて複数の供給先に輸送されて入荷した複数銘柄の配合原材料を、各供給先において配合して使用する際に、当該複数銘柄の配合原材料の配合計画、及び当該複数銘柄の配合原材料を、積地である複数の供給元から揚地である複数の供給先に輸送する船舶の配船計画を作成する配合及び配船計画作成システムであって、配合原材料の、供給元毎及び銘柄毎に予め設定された引取目標量に基づいて、複数銘柄の配合原材料を入荷して混合する配合計画を作成する第１の配合計画作成手段と、前記第１の配合計画作成手段により作成された配合計画に基づいて、複数銘柄の配合原材料を複数の積地から複数の揚地に輸送する配船計画を作成する配船計画作成手段と、前記配船計画作成手段により作成された配船計画を格納するデータベース手段とを備えたことを特徴とする配合及び配船計画作成システム。
（２）　前記第１の配合計画作成手段は、配合原材料の引取目標量、配合原材料の在庫状況、配合原材料の性状、配合原材料の購入費用情報、船舶を利用する際の輸送費用情報を含むデータを取り込むデータ取込み手段と、配合原材料の需給状態及び混合後の性状を計算するシミュレータと、配合原材料の需給バランス制約を表す数式モデル、及び、混合後の性状制約を表す数式モデルを構築する数式モデル構築手段と、前記数式モデル構築手段により構築された数式モデルを用いて、配合原材料の購入費用及び輸送費用に関して構築された目的関数に基づいて最適化計算を行い、前記シミュレータに対する指示を算出する最適化計算手段と、前記シミュレータによるシミュレーション結果である配合計画を出力する出力手段とを備えたことを特徴とする上記（１）に記載の配合及び配船計画作成システム。
（３）　前記第１の配合計画作成手段の最適化計算手段では、更に、配合原材料の入荷量と引取目標量との関係に関して構築された目的関数に基づいて最適化計算を行うことを特徴とする上記（２）に記載の配合及び配船計画作成システム。
（４）　前記第１の配合計画作成手段のデータ取込み手段により取り込む輸送費用情報には、船舶別・積港別・揚港別フレートの情報と、銘柄別・揚港別フレートの情報とが含まれることを特徴とする上記（２）又は（３）に記載の配合及び配船計画作成システム。
（５）　前記第１の配合計画作成手段は、作成された配合計画による配合原材料の使用予定量を算出するものであり、前記配船計画作成手段は、前記第１の配合計画作成手段により作成された配合計画による配合原材料の使用予定量、引取目標量、契約の種別の異なる船舶がリストアップされた船舶リスト、前記船舶リストにリストアップされている船舶の運航状況、配合原材料の在庫状況、配合原材料の購入費用情報、前記船舶リストにリストアップされている船舶を利用する場合の輸送費用情報を含むデータを取り込むデータ取込み手段と、配合原材料の在庫推移を計算する在庫推移シミュレータ、及び、船舶運航状況の推移を計算する船舶運航状況推移シミュレータにより構成されるシミュレータと、前記船舶運航状況に基づいて前記船舶リストから船舶を選択し、必要な船舶財源を作成する船舶財源作成手段と、少なくとも前記船舶財源作成手段により作成された船舶の運航制約、揚地での配合原材料の需給バランス制約、及び引取目標量制約を表す数式モデルを構築する数式モデル構築手段と、前記数式モデル構築手段により構築された数式モデルを用いて、少なくとも輸送費用に関して構築された目的関数に基づいて最適化計算を行い、前記シミュレータに対する指示を算出する最適化計算手段と、前記シミュレータによるシミュレーション結果である配船計画を出力する出力手段とを備えたことを特徴とする上記（１）乃至（４）のいずれか１つに記載の配合及び配船計画作成システム。
（６）　前記配船計画作成手段は、前記第１の配合計画作成手段により作成された配合計画による配合原材料の使用予定量、引取目標量、契約の種別の異なる船舶がリストアップされた船舶リスト、前記船舶リストにリストアップされている船舶の運航状況、前記性状が近い銘柄をグループ化して取り扱った配合原材料の在庫状況、配合原材料の購入費用情報、前記船舶リストにリストアップされている船舶を利用する場合の輸送費用情報を含むデータを取り込むデータ取込み手段と、前記性状が近い銘柄をグループ化して取り扱う配合原材料の在庫推移を計算する在庫推移シミュレータ、及び、船舶運航状況の推移を計算する船舶運航状況推移シミュレータにより構成されるシミュレータと、前記船舶運航状況に基づいて前記船舶リストから船舶を選択し、必要な船舶財源を作成する船舶財源作成手段と、少なくとも前記船舶財源作成手段により作成された船舶の運航制約、揚地での前記性状が近い銘柄をグループ化して取り扱う配合原材料の需給バランス制約、及び引取目標量制約を表す数式モデルを構築する数式モデル構築手段と、前記数式モデル構築手段により構築された数式モデルを用いて、少なくとも輸送費用に関して構築された目的関数に基づいて最適化計算を行い、前記シミュレータに対する指示を算出する最適化計算手段と、前記シミュレータによるシミュレーション結果である配船計画を出力する出力手段とを備えたことを特徴とする上記（１）乃至（４）のいずれか１つに記載の配合及び配船計画作成システム。
（７）　前記配船計画作成手段により作成された配船計画に基づいて、複数銘柄の配合原材料を入荷して混合する配合計画を作成する第２の配合計画作成手段と、前記第２の配合計画作成手段により作成された配合計画を格納するデータベース手段とを備えたことを特徴とする上記（１）乃至（６）のいずれか１つに記載の配合及び配船計画作成システム。
（８）　前記第２の配合計画作成手段は、前記配船計画作成手段により作成された配船計画による配合原材料の入荷予定、配合原材料の在庫状況、配合原材料の性状、配合原材料の購入費用情報、船舶を利用する際の輸送費用情報を含むデータを取り込むデータ取込み手段と、配合原材料の需給状態及び混合後の性状を計算するシミュレータと、配合原材料の需給バランス制約を表す数式モデル、及び、混合後の性状制約を表す数式モデルを構築する数式モデル構築手段と、前記数式モデル構築手段により構築された数式モデルを用いて、配合原材料の購入費用及び輸送費用に関して構築された目的関数に基づいて最適化計算を行い、前記シミュレータに対する指示を算出する最適化計算手段と、前記シミュレータによるシミュレーション結果である配合計画を出力する出力手段とを備えたことを特徴とする上記（７）に記載の配合及び配船計画作成システム。
（９）　前記第１の配合計画作成手段により作成された配合計画を、性状が近い銘柄をグループ化して取り扱うことを特徴とする上記（１）乃至（８）のいずれか１つに記載の配合及び配船計画作成システム。
（１０）　複数の供給元から船舶にて複数の供給先に輸送されて入荷した複数銘柄の配合原材料を、各供給先において配合して使用する際に、当該複数銘柄の配合原材料の配合計画、及び当該複数銘柄の配合原材料を、積地である複数の供給元から揚地である複数の供給先に輸送する船舶の配船計画を作成する配合及び配船計画作成方法であって、第１の配合計画作成手段が、配合原材料の、供給元毎及び銘柄毎に予め設定された引取目標量に基づいて、複数銘柄の配合原材料を入荷して混合する配合計画を作成するステップと、配船計画作成手段が、前記第１の配合計画作成手段により作成された配合計画に基づいて、複数銘柄の原材料を複数の積地から複数の揚地に輸送する配船計画を作成するステップと、前記配船計画作成手段により作成された配船計画をデータベース手段に格納するステップとを有することを特徴とする配合及び配船計画作成方法。
（１１）　上記（１）乃至（９）のいずれか１つに記載の配合及び配船計画作成システムの各手段としてコンピュータを機能させるためのプログラム。 The present invention further includes the following aspects.
(1) When blending raw materials of multiple brands that have been transported and received from multiple suppliers to multiple suppliers by ship, and used at each supplier, the blending plan of the blended raw materials of the multiple brands, And a blending and dispatching plan creation system for creating a ship allocation plan for a ship that transports a plurality of blended raw materials from a plurality of suppliers that are loading sites to a plurality of destinations that are landing sites, First blending plan creation means for creating a blending plan for receiving and mixing a plurality of branded blending raw materials based on a take-up target amount preset for each supplier and each brand, and the first blending Based on the blending plan created by the plan creating means, a ship assignment plan creating means for creating a ship assignment plan for transporting a plurality of branded raw materials from a plurality of loading sites to a plurality of landing sites, and the ship assignment plan creating means The ship allocation plan created by A blending and ship allocation plan creation system characterized by comprising database means for storing.
(2) The first blending plan creation means includes data including a target amount of blended raw materials, a stock status of blended raw materials, properties of blended raw materials, purchase cost information of blended raw materials, and transport cost information when using a ship. Data acquisition means for importing, a simulator for calculating the supply and demand status of blended raw materials and properties after mixing, a mathematical model that represents the balance between supply and demand of blended raw materials, and a mathematical model that constructs a mathematical model that represents the property constraints after mixing Optimizing by calculating an instruction to the simulator by performing an optimization calculation based on an objective function constructed with respect to the purchase cost and transportation cost of the compounding raw material using the construction model and the mathematical model constructed by the mathematical model construction means A calculation calculation means, and an output means for outputting a blending plan as a simulation result by the simulator. The composition and ship allocation plan creation system according to (1) above, which is characterized.
(3) The optimization calculation means of the first blending plan creation means further performs optimization calculation based on an objective function constructed with respect to the relationship between the amount of blended raw materials received and the take-up target amount. The composition and ship allocation plan creation system according to (2) above.
(4) The transportation cost information fetched by the data fetching means of the first blend plan creation means includes freight information by ship, by port, by port, and by freight by brand and by port. The composition and ship allocation plan creation system as described in (2) or (3) above.
(5) The first blending plan creation means calculates a planned use amount of the blended raw materials based on the created blending plan, and the ship allocation plan creation means is created by the first blending plan creation means. The planned use amount of the compounded raw material according to the formulated composition plan, the target amount of collection, the ship list in which the ships with different contract types are listed, the operational status of the ships listed in the ship list, the stock status of the compounded raw materials, Data acquisition means for fetching data including purchase cost information of compounded raw materials, transport cost information when using a ship listed in the ship list, an inventory transition simulator for calculating inventory transition of compounded raw materials, and a ship A simulator composed of a ship operation status transition simulator for calculating the transition of the operation status, and the ship based on the ship operation status Ship funding source creation means for selecting a ship from the ship list and creating necessary ship funding sources, at least ship operation constraints created by the ship funding source creation means, supply and demand balance restrictions for compound raw materials at the landing site, and takeover target Formula simulator that builds a formula model that expresses a quantity constraint, and the simulator that performs optimization calculation based on an objective function that is built at least with respect to transportation costs, using the formula model built by the formula model construction means Any one of the above (1) to (4), further comprising: an optimization calculation unit that calculates an instruction to the ship and an output unit that outputs a ship allocation plan that is a simulation result by the simulator. Formulation and ship planning system.
(6) The ship allocation plan creating means is a ship list in which ships with different planned raw material usage amounts, take-up target quantities, and contract types according to the blending plan created by the first blending plan creating means are listed. The operation status of the vessels listed in the vessel list, the inventory status of the compounded raw materials handled by grouping the brands with similar properties, the purchase cost information of the compounded raw materials, and the vessels listed in the vessel list Data capture means for capturing data including transportation cost information when used, inventory transition simulator for calculating inventory transition of compound raw materials handled by grouping brands with similar properties, and ship for calculating transition of ship operation status From the ship list based on the simulator configured by the operation status transition simulator and the vessel operation status A selection of vessels, and a vessel funding creation means that creates the necessary vessel funding, and a combination raw material that handles at least the ship operation restrictions created by the vessel funding creation means and the brands with similar properties at the landing site. Mathematical model construction means for constructing a mathematical model expressing supply-demand balance constraints and take-up target quantity constraints, and using the mathematical model constructed by the mathematical model construction means, at least based on an objective function constructed with respect to transportation costs (1) to (4), characterized by comprising: optimization calculation means for performing an optimization calculation and calculating an instruction for the simulator; and an output means for outputting a ship assignment plan as a simulation result by the simulator. The composition and ship allocation plan creation system according to any one of the above.
(7) Second blending plan creation means for creating a blending plan for receiving and mixing a plurality of branded blending raw materials based on the dispatching plan created by the dispatching plan creation means, and the second blending The blending / shipping plan creation system according to any one of (1) to (6), further comprising database means for storing the blending plan created by the plan creation means.
(8) The second blending plan creation means includes the arrival schedule of the blended raw materials, the stock status of the blended raw materials, the properties of the blended raw materials, and the purchase cost information of the blended raw materials. , Data capturing means for capturing data including transportation cost information when using a ship, a simulator for calculating the supply and demand status of mixed raw materials and properties after mixing, a mathematical model representing the supply and demand balance constraints of mixed raw materials, and mixing Mathematical model construction means for constructing a mathematical model that represents the subsequent property constraint, and using the mathematical model constructed by the mathematical model construction means, it is optimal based on the objective function constructed for the purchase cost and the transportation cost of the compounded raw materials Optimization calculation means for performing calculation and calculating instructions for the simulator, and simulation results by the simulator The composition and ship allocation plan creation system according to the above (7), further comprising an output means for outputting the composition plan.
(9) The formulation according to any one of (1) to (8), wherein the formulation plan created by the first formulation plan creation means is handled by grouping brands having similar properties. And a ship planning system.
(10) When a plurality of branded raw materials that have been transported and received from a plurality of suppliers to a plurality of destinations by ship are mixed and used at each supplier, And a blending / shipping plan creation method for creating a ship dispatching plan for a ship that transports the plurality of brands of blended raw materials from a plurality of supply sources that are loading sites to a plurality of supply destinations that are landing sites, A step of creating a blending plan in which a plurality of branded blended raw materials are received and mixed based on a take-up target amount set in advance for each supplier and brand of blended raw materials; A plan creation means creating a ship allocation plan for transporting a plurality of brands of raw materials from a plurality of loading sites to a plurality of landings based on the blending plan created by the first blending plan creation means; Created by ship allocation plan creation means And a step of storing the formed ship allocation plan in a database means.
(11) A program for causing a computer to function as each means of the composition and ship allocation plan creation system according to any one of (1) to (9) above.
　本発明によれば、複数銘柄の配合原材料を入荷して混合する配合計画、及び複数銘柄の原材料を複数の積地から複数の揚地に輸送する配船計画を、相互に連係させ、一括して作成することができる。特に、傭船契約の種別の異なる船舶を考慮して、輸送費用に関して構築した目的関数を用意し、この目的関数に基づいて最適化計算を行うことができる。このことにより、船舶の傭船契約の種類（連続航海船、不定期船、スポット船）、船団の構成を決める船舶を雇うか、雇わないかまでも含めた、輸送費用のミニマム化のための計画立案が可能になる。更には、配合計画段階においても、輸送費用をミニマム化することを考慮した計画の作成が可能となる。 According to the present invention, a blending plan for receiving and mixing a plurality of branded raw materials and a ship allocation plan for transporting a plurality of branded raw materials from a plurality of loading sites to a plurality of landing sites are linked to each other and collectively. Can be created. In particular, it is possible to prepare an objective function constructed with respect to transportation costs in consideration of ships with different types of chartering contracts, and perform optimization calculation based on this objective function. As a result, the plan for minimizing transportation costs, including whether or not to hire a ship that determines the type of dredger contract (continuous cruise ship, irregular ship, spot ship), and fleet composition. Planning is possible. Furthermore, it is possible to create a plan in consideration of minimizing the transportation cost even at the formulation planning stage.
　１００：第１の配合計画作成装置
　２００：配船計画作成装置
　２０１：シミュレータ
　２０２：マクロ最適化部
　２０２ａ：船舶財源リスト作成部
　２０２ｂ：数式モデル設定部
　２０２ｃ：最適化計算部
　２０３：ミクロ最適化部
　２０３ａ：数式モデル設定部
　２０３ｂ：最適化計算部
　２０４：データ取込み部
　２０５：出力部
　３００：第２の配合計画作成装置
　３０３：表示部
　３０４：操業者評価部
　３１２：性状シミュレータ
　３１３：需給バランスモデル設定部
　３１４：性状モデル設定部
　３１５：計画部
　３５１：入力データ取込み部
　３５２：計画作成期間設定部
　３５３：時間精度設定部
　３５４：最適化期間設定部
　３５５：計画確定期間設定部
　３５６：需給バランスモデル設定部
　３５７：性状モデル設定部
　３５７ａ：線形化部
　３５８：固定化抽出処理部
　３５９：配合計画求解部
　３６０：確認部
　３６１：在庫推移シミュレータ
　３６２：性状シミュレータ
　３６３：確定部
　３６４：判定部
　３６５：更新部
　３６６：出力部
　４００：データベース
　５００：コンピュータ DESCRIPTION OF SYMBOLS 100: 1st mixing | blending plan preparation apparatus 200: Ship allocation plan preparation apparatus 201: Simulator 202: Macro optimization part 202a: Ship financial resource list preparation part 202b: Formula model setting part 202c: Optimization calculation part 203: Micro optimization part 203a: Formula model setting unit 203b: Optimization calculation unit 204: Data acquisition unit 205: Output unit 300: Second formulation plan creation device 303: Display unit 304: Operator evaluation unit 312: Property simulator 313: Supply / demand balance model setting Unit 314: property model setting unit 315: planning unit 351: input data capturing unit 352: plan creation period setting unit 353: time accuracy setting unit 354: optimization period setting unit 355: plan determination period setting unit 356: supply / demand balance model setting Part 357: property model setting part 357a: linearization part 358: fixed Extraction processing unit 359: blending plan solution finding unit 360: confirmation unit 361: inventory transition simulator 362: Properties simulator 363: fixing unit 364: the determination unit 365: updating unit 366: output unit 400: Database 500: Computer

Claims (14)

  1. 　複数の銘柄の配合原材料を、複数の積地から複数の揚地に輸送する配船計画と；
    　入荷した前記配合原材料を、それぞれの前記揚地で混合する配合計画と；
    　を作成する配合及び配船計画作成システムであって：
    　前記積地毎及び前記銘柄毎に予め設定された前記配合原材料の引取目標量に基づいて、第１の配合計画を作成する第１の配合計画作成部と；
    　作成された前記第１の配合計画に基づいて、前記配船計画を作成する配船計画作成部と；
    　作成された前記配船計画を格納するデータベース部と；
    　を備えることを特徴とする配合及び配船計画作成システム。     A ship allocation plan for transporting multiple brands of blended raw materials from multiple loading sites to multiple landing sites;
    A blending plan for mixing the received blended raw materials at each landing site;
    A blending and shipping plan creation system that creates:
    A first blending plan creation unit that creates a first blending plan based on a take-up target amount of the blending raw material set in advance for each loading place and each brand;
    A ship assignment plan creation unit for creating the ship assignment plan based on the created first composition plan;
    A database unit for storing the created ship allocation plan;
    A composition and ship planning plan creation system characterized by comprising:
  2. 　前記配合計画の作成と、前記配船計画の作成と、において、前記引取目標量および前記配合原材料の在庫状況に関して共通の制約条件が用いられることを特徴とする請求項１に記載の配合及び配船計画作成システム。 The composition and distribution according to claim 1, wherein a common constraint condition is used in the preparation of the combination plan and the preparation of the ship allocation plan with respect to the take-up target amount and the stock status of the combination raw materials. Ship planning system.
  3. 　前記第１の配合計画作成部は：
    　前記配合原材料の前記引取目標量、前記配合原材料の在庫状況、前記配合原材料の性状、前記配合原材料の購入費用、及び、前記配合原材料の輸送費用を含むデータを取り込むデータ取込み部と；
    　前記配合原材料の需給バランス制約、及び、前記配合原材料の混合後の性状制約を表す数式モデルをそれぞれ設定する数式モデル設定部と；
    　設定された前記数式モデルを用いて、前記購入費用及び前記輸送費用に関して予め構築された目的関数に基づいて最適化計算を行う最適化計算部と；
    　前記最適化計算の結果に基づいて動作し、前記配合原材料の需給状態の推移、及び、前記配合原材料の混合後の前記性状の推移をシミュレートするシミュレータと；
    　前記シミュレータによるシミュレーション結果である配合計画を出力する出力部と；
    　を備えることを特徴とする請求項１に記載の配合及び配船計画作成システム。     The first formulation planning section is:
    A data capturing unit that captures data including the target amount of the blended raw material, the inventory status of the blended raw material, the properties of the blended raw material, the purchase cost of the blended raw material, and the transportation cost of the blended raw material;
    A formula model setting unit for respectively setting a formula model representing a supply and demand balance constraint of the blended raw material and a property constraint after mixing of the blended raw material;
    An optimization calculation unit that performs an optimization calculation based on an objective function that is constructed in advance with respect to the purchase cost and the transportation cost, using the set mathematical model;
    A simulator that operates based on the result of the optimization calculation and simulates the transition of the supply and demand state of the blended raw materials and the transition of the properties after mixing of the blended raw materials;
    An output unit for outputting a blending plan which is a simulation result by the simulator;
    The blending and ship allocation plan creation system according to claim 1, comprising:
  4. 　前記最適化計算部が、前記配合原材料の入荷量と前記引取目標量との関係に関して予め構築された目的関数に更に基づいて最適化計算を行うことを特徴とする請求項３に記載の配合及び配船計画作成システム。 The said optimization calculation part performs the optimization calculation further based on the objective function constructed | assembled previously regarding the relationship between the arrival amount of the said mixing | blending raw material, and the said take-off target amount, The mixing | blending of Claim 3 characterized by the above-mentioned Ship planning plan creation system.
  5. 　前記データ取込み部により取り込まれる前記輸送費用には；
    　船舶別・積港別・揚港別のフレートの情報と；
    　前記銘柄別・揚港別フレートの情報と；
    　が含まれることを特徴とする請求項３又は４に記載の配合及び配船計画作成システム。     The transportation costs captured by the data capture unit include:
    Information on freight by ship / ship port / lift port;
    Information on the freight by brand / shipping port;
    The blending and ship allocation plan creation system according to claim 3 or 4, characterized in that
  6. 　前記第１の配合計画作成部は、更に、作成された前記配合計画に従った前記配合原材料の使用予定量を算出し、
    　前記配船計画作成部は：
    　前記配合原材料の前記使用予定量、前記配合原材料の前記引取目標量、前記配合原材料の在庫状況、前記配合原材料の購入費用、複数の種別の傭船契約に基づいて運用される複数の船舶がリストアップされた船舶リスト、各々の前記船舶の運航状況、及び、輸送費用、を含むデータを取り込むデータ取込み部と；
    　前記運航状況に基づいて前記船舶リストから配船対象の候補となる前記船舶を選択し、船舶財源リストを作成する船舶財源リスト作成部と；
    　前記船舶財源リストに含まれる前記船舶の運航制約、前記揚地での前記配合原材料の需給バランス制約、及び、引取目標量制約を表す数式モデルを設定する数式モデル設定部と；
    　設定された前記数式モデルを用いて、前記輸送費用に関して予め構築された目的関数に基づいて最適化計算を行う最適化計算部と；
    　前記在庫状況の推移をシミュレートする在庫推移シミュレータと、前記運航状況の推移をシミュレートする船舶運航状況推移シミュレータと、を含み、前記最適化計算の結果に基づいて動作する、シミュレータと；
    　前記シミュレータによるシミュレーション結果である前記配船計画を出力する出力部と；
    　を備えることを特徴とする請求項１に記載の配合及び配船計画作成システム。     The first blending plan creation unit further calculates a planned use amount of the blended raw materials according to the created blending plan,
    The Ship Planning Plan Department:
    List of the planned use amount of the blended raw material, the target amount of the blended raw material, the inventory status of the blended raw material, the purchase cost of the blended raw material, and a plurality of vessels operated based on multiple types of chartering contracts A data fetching unit for fetching data including the ship list, the operational status of each ship, and the transportation cost;
    A ship financial resource list creation unit that selects the ship that is a candidate for dispatch from the ship list based on the operational status, and creates a ship financial resource list;
    A mathematical expression model setting unit for setting a mathematical expression model representing operational restrictions of the ship included in the ship financial resource list, supply and demand balance restrictions of the blended raw materials at the landing, and takeover target quantity restrictions;
    An optimization calculation unit that performs an optimization calculation based on an objective function that is constructed in advance with respect to the transportation cost, using the set mathematical model;
    A simulator that includes an inventory transition simulator that simulates the transition of the inventory status and a ship operation status transition simulator that simulates the transition of the operational status, and that operates based on the result of the optimization calculation;
    An output unit for outputting the ship allocation plan which is a simulation result by the simulator;
    The blending and ship allocation plan creation system according to claim 1, comprising:
  7. 　前記第１の配合計画作成部は、更に、作成された前記配合計画に従った前記配合原材料の使用予定量を算出し、
    　前記配船計画作成部は：
    　前記配合原材料の前記使用予定量、前記配合原材料の前記引取目標量、性状が規定した範囲に含まれる複数の前記銘柄の前記配合原材料がグループ化されて取り扱われたグループ化配合原材料の在庫状況、前記配合原材料の購入費用、複数の種別の傭船契約に基づいて運用される複数の船舶がリストアップされた船舶リスト、各々の前記船舶の運航状況、及び、輸送費用、を含むデータを取り込むデータ取込み部と；
    　前記運航状況に基づいて前記船舶リストから配船対象の候補となる前記船舶を選択し、船舶財源リストを作成する船舶財源リスト作成部と；
    　前記船舶財源リストに含まれる前記船舶の運航制約、性状が規定した範囲に含まれる複数の前記銘柄の前記配合原材料がグループ化されて取り扱われた前記グループ化配合原材料の需給バランス制約、及び、引取目標量制約を表す数式モデルを設定する数式モデル設定部と；
    　設定された前記数式モデルを用いて、前記輸送費用に関して予め構築された目的関数に基づいて最適化計算を行う最適化計算部と；
    　性状が規定した範囲に含まれる複数の前記銘柄の前記配合原材料がグループ化されて取り扱われた前記グループ化配合原材料の前記在庫状況の推移をシミュレートする在庫推移シミュレータと、前記運航状況の推移をシミュレートする船舶運航状況推移シミュレータと、を含み、前記最適化計算の結果に基づいて動作する、シミュレータと；
    　前記シミュレータによるシミュレーション結果である前記配船計画を出力する出力部と；
    　を備えることを特徴とする請求項１に記載の配合及び配船計画作成システム。     The first blending plan creation unit further calculates a planned use amount of the blended raw materials according to the created blending plan,
    The Ship Planning Plan Department:
    The planned use amount of the blended raw material, the take-up target amount of the blended raw material, the stock status of the grouped blended raw material in which the blended raw materials of the plurality of brands included in the range defined by the properties are handled as a group, Data acquisition that captures data including the purchase cost of the blended raw material, a ship list that lists a plurality of ships that are operated based on a plurality of types of chartering contracts, the operational status of each ship, and transportation costs Part;
    A ship financial resource list creation unit that selects the ship that is a candidate for dispatch from the ship list based on the operational status, and creates a ship financial resource list;
    Constraints on supply and demand of the grouped blended raw materials in which the blended raw materials of a plurality of the brands included in the range specified by the properties included in the ship funding list are handled as a group A formula model setting unit for setting a formula model representing a target quantity constraint;
    An optimization calculation unit that performs an optimization calculation based on an objective function that is constructed in advance with respect to the transportation cost, using the set mathematical model;
    A stock transition simulator that simulates a transition of the inventory status of the grouped blended raw materials that are handled by grouping the blended raw materials of a plurality of the brands included in the range defined by the properties, and a transition of the operation status A simulator for simulating ship operation status, and a simulator that operates based on the result of the optimization calculation;
    An output unit for outputting the ship allocation plan which is a simulation result by the simulator;
    The blending and ship allocation plan creation system according to claim 1, comprising:
  8. 　作成された前記配船計画に基づいて、第２の配合計画を作成する第２の配合計画作成部を更に備え、
    　前記データベース部は前記第２の配合計画を更に格納することを特徴とする請求項１，３，７のいずれか一項に記載の配合及び配船計画作成システム。     Based on the created ship allocation plan, further comprising a second blending plan creation unit for creating a second blending plan,
    The said database part further stores the said 2nd mixing | blending plan, The mixing | blending and ship allocation plan preparation system as described in any one of Claim 1, 3, 7 characterized by the above-mentioned.
  9. 　前記第２の配合計画作成部は：
    　作成された前記配船計画に基づく前記配合原材料の入荷予定、前記配合原材料の在庫状況、前記配合原材料の性状、前記配合原材料の購入費用、及び、前記配合原材料の輸送費用を含むデータを取り込む第２のデータ取込み部と；
    　前記入荷予定を用い、前記配合原材料の需給バランス制約、及び、前記配合原材料の混合後の性状制約を表す数式モデルをそれぞれ設定する第２の数式モデル設定部と；
    　設定された前記数式モデルを用いて、前記購入費用及び前記輸送費用に関して予め構築された目的関数に基づいて最適化計算を行う第２の最適化計算部と；
    　前記最適化計算の結果に基づいて動作し、前記配合原材料の需給状態の推移、及び、前記配合原材料の混合後の前記性状の推移をシミュレートする第２のシミュレータと；
    　前記シミュレータによるシミュレーション結果である配合計画を出力する第２の出力部と；
    　を備えることを特徴とする請求項８に記載の配合及び配船計画作成システム。     The second formulation planning section is:
    Based on the schedule of arrival of the blended raw material based on the prepared ship allocation plan, the inventory status of the blended raw material, the properties of the blended raw material, the purchase cost of the blended raw material, and the data including the transportation cost of the blended raw material 2 data acquisition units;
    A second mathematical model setting unit that sets the mathematical model representing the supply-demand balance constraint of the blended raw material and the property constraint after mixing of the blended raw material using the arrival schedule;
    A second optimization calculation unit that performs an optimization calculation based on an objective function that is constructed in advance with respect to the purchase cost and the transportation cost, using the set mathematical model;
    A second simulator that operates based on the result of the optimization calculation and simulates the transition of the supply and demand state of the blended raw material and the transition of the property after mixing of the blended raw material;
    A second output unit for outputting a blending plan which is a simulation result by the simulator;
    The blending and ship allocation plan creation system according to claim 8.
  10. 　前記第１の配合計画の前記需給バランス制約と、前記第２の配合計画の前記需給バランス制約と、において、需要に関して同一の制約条件を設定し；
    　前記第１の配合計画の前記需給バランス制約において、供給に関して、引取目標量を制約条件として設定し；
    　前記第２の配合計画の前記需給バランス制約において、供給に関して、船舶による原材料の入荷量を制約条件として設定する；
    　ことを特徴とする請求項９に記載の配合及び配船計画作成システム。     In the supply / demand balance constraint of the first blending plan and the supply / demand balance constraint of the second blending plan, the same constraint condition is set for demand;
    In the supply-demand balance constraint of the first blending plan, with regard to supply, a take-up target amount is set as a constraint condition;
    In the supply-demand balance constraint of the second blending plan, regarding the supply, the amount of raw materials received by the ship is set as a constraint condition;
    The composition and ship allocation plan creation system according to claim 9.
  11. 　前記第１の配合計画の前記目的関数において、銘柄別・揚港別見做しフレートを用い；
    　前記第２の配合計画の前記目的関数において、船舶別・積港別・揚港別フレートを用いる、ことを特徴とする請求項９に記載の配合及び配船計画作成システム。     In the objective function of the first compounding plan, using the freight rate by brand / shipping port;
    10. The composition and ship allocation plan creation system according to claim 9, wherein freight by ship, ship by port, and ship by port are used in the objective function of the second composition plan.
  12. 　作成された前記配合計画において、性状が規定した範囲に含まれる複数の前記銘柄の前記配合原材料がグループ化されて取り扱われることを特徴とする請求項１又は３に記載の配合及び配船計画作成システム。 4. The composition and ship allocation plan creation according to claim 1 or 3, wherein in the created composition plan, the composition raw materials of a plurality of the brands included in a range defined by properties are handled as a group. system.
  13. 　複数の銘柄の配合原材料を、複数の積地から複数の揚地に輸送する配船計画と；
    　入荷した前記配合原材料を、それぞれの前記揚地で混合する配合計画と；
    　を作成する配合及び配船計画作成方法であって：
    　前記積地毎及び前記銘柄毎に予め設定された前記配合原材料の引取目標量に基づいて、第１の配合計画を作成する第１の配合計画作成工程と；
    　作成された前記第１の配合計画に基づいて、前記配船計画を作成する配船計画作成工程と；
    　作成された前記配船計画をデータベースに格納するデータベース格納工程と；
    　を備えることを特徴とする配合及び配船計画作成方法。     A ship allocation plan for transporting multiple brands of blended raw materials from multiple loading sites to multiple landing sites;
    A blending plan for mixing the received blended raw materials at each landing site;
    A recipe and ship planning method for creating:
    A first blending plan creation step for creating a first blending plan based on a target amount of the blending raw material set in advance for each loading place and each brand;
    A ship assignment plan creation step of creating the ship assignment plan based on the created first composition plan;
    A database storage step of storing the created ship allocation plan in a database;
    A composition and a ship allocation plan creation method characterized by comprising:
  14. 　請求項１に記載の配合及び配船計画作成システムの各部分としてコンピュータを機能させるためのプログラム。 A program for causing a computer to function as each part of the composition and ship allocation plan creation system according to claim 1.
PCT/JP2009/006236 2008-11-21 2009-11-19 System, method and program for making composition plan and allocation of ships WO2010058584A1 (en)

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BRPI0920989A BRPI0920989A2 (en) 2008-11-21 2009-11-19 system for setting up a combination plan and ship endowment, method and program for the same
KR1020117004465A KR101296933B1 (en) 2008-11-21 2009-11-19 System, method and storage medium for storing program for making composition plan and allocation of ships
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