CN113627643A - Multi-type intermodal ship and yard unmanned truck-concentration scheduling optimization method - Google Patents

Multi-type intermodal ship and yard unmanned truck-concentration scheduling optimization method Download PDF

Info

Publication number
CN113627643A
CN113627643A CN202110718074.9A CN202110718074A CN113627643A CN 113627643 A CN113627643 A CN 113627643A CN 202110718074 A CN202110718074 A CN 202110718074A CN 113627643 A CN113627643 A CN 113627643A
Authority
CN
China
Prior art keywords
unmanned
ship
truck
representing
port
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110718074.9A
Other languages
Chinese (zh)
Inventor
张永
吴光涛
赵越
周博见
鲍香台
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southeast University
Original Assignee
Southeast University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southeast University filed Critical Southeast University
Priority to CN202110718074.9A priority Critical patent/CN113627643A/en
Publication of CN113627643A publication Critical patent/CN113627643A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Marketing (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Educational Administration (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a multi-type intermodal unmanned truck-concentration scheduling optimization method between a ship and a yard, which comprises the following steps: the method comprises the following steps: according to an operation plan, combining the full-load driving times of the unmanned truck, the loading and unloading times of a shore bridge and a field bridge and the distance between transportation nodes, and taking the lowest transportation cost as an optimization target to construct an unmanned truck dispatching model; step two: setting the walking times of the unmanned container truck between the ship entering the port and the ship leaving the port, further simplifying the model, and solving by adopting a dynamic planning algorithm to obtain the dynamic planning data of the operation plan of the unmanned container truck; step three: and checking and optimizing for multiple times through actual operation to obtain the optimal route of the unmanned card concentrator. The method is optimized aiming at unmanned truck-collecting scheduling between a ship and a storage yard in a closed scene, solves the problem of unmanned truck-collecting scheduling in a station by taking public-water combined transportation common in an automatic dock as a background, verifies and analyzes the model, verifies the feasibility and operability of the method, and has important significance.

Description

Multi-type intermodal ship and yard unmanned truck-concentration scheduling optimization method
Technical Field
The invention belongs to the technical field of unmanned truck concentration application in a closed scene, and particularly relates to a multi-mode intermodal ship and yard unmanned truck concentration scheduling optimization method.
Background
The development of an automatic wharf enables a truck-collecting dispatching mode based on a 'working face' to have a realization basis, and the application of an unmanned technology can better improve dispatching efficiency and accuracy. The main processes of loading and unloading of the container ship of the common automatic wharf at present are as follows: containers to be unloaded of ships entering the port are transported to the unmanned container trucks through the shore bridges, transported to the import box areas of the storage yards specified by the operation plans through the unmanned container trucks, and unloaded to the shore or transported to the export box areas through the yard bridges to be unloaded to ships leaving the port. After the container is loaded on the ship, the unmanned truck can continue to go to an exit box area to take the container or return to an incoming ship to take the container. Through the analysis, the unmanned card set based on the dynamic scheduling of the 'operation surface' has various routes for horizontal transportation operation:
(1) the unmanned collecting card is loaded with an inlet box by a shore bridge, is conveyed to an inlet box area of a storage yard specified by an operation plan, and then returns to a berth in an empty box; (2) the unmanned collecting card is loaded with an inlet box by a shore bridge, conveyed to an inlet box area of a storage yard specified by an operation plan, then moved to an outlet box area to take boxes, conveyed to a ship for port departure and berthed for shipment; (3) the unmanned container truck loads an outlet box in an outlet box area of the storage yard to a ship for ship loading at the port of departure, and then the empty box returns to the storage yard; (4) the unmanned container truck loads an outlet box in the outlet box area of the storage yard to the ship for loading at the port of departure, and then the empty box goes to the ship for berthing at the port of arrival.
After the ship enters a port, the unloaded containers are transported to the unmanned container trucks through the shore bridge, the unmanned container trucks transport the containers to a storage yard and then empty containers return to a berth or empty containers are taken and then return to the berth, and the process forms a walking loop for horizontal transportation of the unmanned container trucks in the port. In addition, at present, a plurality of storage yards are clearly divided into an inlet box area and an outlet box area, so that the unmanned container trucks can be more conveniently dispatched according to the box area numbers. However, at present, the problem of dispatching the trucks between ships and yards is still the most common problem of dispatching the horizontal transportation operation inside the automated wharf, and is also an important link for connecting the public water transportation with ports as hubs.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a scheduling optimization method for unmanned trucks between ships and yards for multi-type intermodal transportation, which aims to solve the problem of unmanned truck-collecting scheduling between ships and yards in the existing closed scene, reduce the total time of truck-collecting transportation operation and reduce the operation cost by reducing the total distance of truck-collecting driving, and achieve the aim of optimizing the minimum operation cost of unmanned trucks.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: an unmanned truck-collecting dispatching optimization method between a multi-type intermodal ship and a yard comprises the following steps:
the method comprises the following steps: according to an operation plan, combining the full-load driving times of the unmanned truck, the loading and unloading times of a shore bridge and a field bridge and the distance between transportation nodes, and taking the lowest transportation cost as an optimization target to construct an unmanned truck dispatching model;
step two: setting the walking times of the unmanned container truck between the ship entering the port and the ship leaving the port, further simplifying the model, and solving by adopting a dynamic planning algorithm to obtain the dynamic planning data of the operation plan of the unmanned container truck;
step three: and checking and optimizing for multiple times through actual operation to obtain the optimal route of the unmanned card concentrator.
Further, in the first step, with the lowest transportation cost as an optimization target, an unmanned truck dispatching model is constructed, and the shortest total time of an unmanned truck no-load mode is set as a target function Z, which is specifically shown in a formula (1-1):
Figure BDA0003135778780000021
wherein A represents a container ship set; e represents a container import yard box area set; def、Dae、Dbf、DbaRepresenting the distances between nodes e and f, nodes a and e, nodes b and f, and nodes b and a; v. ofdRepresenting the speed of the unmanned truck when empty; qefIndicates no-load transportation operation of unmanned trucks from an inlet box area e to an outlet box area fThe number of times; qeaRepresenting the number of times of no-load transportation operation of the unmanned container truck from the inlet box area e to the port-entering ship a; qbfRepresenting the number of times of no-load transportation operation of the unmanned truck from the ship b at the port to the box area f at the outlet; qbaIndicating the number of times the unmanned truck is unloaded from the ship b to the ship a.
Further, in the second step, when the model is simplified, the specific method is as follows:
the setting is that the walking times between the ship entering the port and the ship leaving the port when the unmanned card is collected is as follows: qbaWhen min (M, N), the objective function Z is simplified to:
Figure BDA0003135778780000022
where M represents the number of containers that the inbound ship a needs to unload and N represents the number of containers in the yard box area that need to be loaded onto the outbound ship b.
Further, the constraint condition of the objective function Z is set as
∑e∈EQae＝M (1-3)，
∑f∈FQfb＝N (1-4)，
M+N＝Q (1-5)；
Formula (1-3) representing that the number of times that the unmanned hub transfers the containers unloaded by the inbound ship a to the inbound box area is equal to the number of containers to be unloaded by the inbound ship; m represents the number of containers to be unloaded by the inbound ship a;
the formula (1-4) represents that the times that the unmanned truck transfers the containers to be loaded to the ship b from the yard exit box area are equal to the number of the containers to be loaded by the ship b; n represents the number of containers in the yard box area that need to be loaded onto the ship b at the port of departure;
the formula (1-5) represents that the total container amount of the unmanned truck transportation operation is the sum of the number of containers to be unloaded by the ship a entering the port and the number of containers to be loaded by the ship b leaving the port; q represents the total bin volume for the unmanned truck transport operation.
Further, in the second step, when the dynamic programming algorithm obtains the dynamic programming data, the specific steps are as follows:
(1) setting the operation plan of the unmanned collecting card, which is to complete n operation tasks or n stages in sequence and record the state variable skFor the distribution state of the unmanned collective card at the beginning of the k-stage task, a variable x is decidedkTo complete the unmanned set of k-phase tasks, the decision set is Vk，k＝1,2,3,...,n；
(2) When k phase state variable skAfter the value of the key point is determined, according to a state transfer equation, namely a formula (1-6), the distribution state of the unmanned collecting card after the k-stage task is completed; is given by the formula
Figure BDA0003135778780000031
Wherein
Figure BDA0003135778780000032
Representing a k-phase state transition; sk+1Representing the distribution state of the unmanned card collection after the task of the k stage is completed;
(3) and recording the time of the unmanned truck driving to the task point as a k-stage effect function (1-7) to obtain an objective function formula (1-8), wherein the formula is
Figure BDA0003135778780000033
Figure BDA0003135778780000034
Wherein v iskRepresenting the phase effect of the k phase, namely the empty driving time; diRepresenting the distance between nodes, i representing an empty driving node pair, and i belonging to { ae, ef, bf }; v. ofdRepresenting the speed of the unmanned truck when empty;
(4) obtaining basic equations (1-9) of dynamic planning in an operation plan, and performing n-stage operation according to the dynamically planned data, wherein the equations are as follows:
Figure BDA0003135778780000035
wherein s is0Representing an initial state variable; f. of0(s0) Representing the value of the objective function under the conditions of the initial state variables.
Has the advantages that: the invention provides an unmanned truck-collecting dispatching optimization method between a ship and a storage yard under a closed scene, which takes the most common public water combined transportation of an automatic wharf as a background and solves the unmanned truck-collecting dispatching problem in a station. Specifically, on one hand, through problem analysis, a model is established with the lowest transportation cost as an optimization target. On the other hand, the model is simplified according to the characteristics of the scene and the unmanned collecting card, and a dynamic programming algorithm is adopted for accurate solution. Meanwhile, the model is verified and analyzed through a specific embodiment, the feasibility and operability of the optimization method are also verified, and the method has important significance.
Drawings
Fig. 1 is a schematic distribution diagram of a storage yard box area in an embodiment of the present invention.
Detailed Description
The following examples are given to further illustrate the embodiments of the present invention. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Before the unmanned truck-mounted scheduling model is constructed, the method meets the assumed conditions: 1) assuming that ships entering and leaving a port need to be loaded and unloaded in a certain period of time, the ships arriving at the port are served simultaneously by coordinating loading and unloading equipment and horizontal transportation equipment, the ships entering the port only carry out ship unloading operation, and the ships leaving the port only carry out ship loading operation; 2) the unmanned collecting cards are arranged on the shore and the box area, and the unmanned collecting cards arranged in the port can be shared among all shore bridges and field bridges; 3) before loading and unloading operation, a corresponding operation plan is made, both the shore bridge and the yard bridge can execute loading and unloading tasks according to the plan, and in order to ensure that the shore bridge is fully utilized, the phenomena that no shore bridge waits for unmanned card collection and no unmanned card collection waits for the shore bridge in the unmanned card collection operation process are assumed; 4) the unmanned container truck can only load one standard container each time, the container truck operates independently, and the operation in a storage yard is smooth and has no congestion phenomenon.
The invention provides an unmanned truck-collecting dispatching optimization method between a multi-type intermodal ship and a yard, which specifically comprises the following steps:
the method comprises the following steps: according to an operation plan, combining the full-load driving times of the unmanned truck, the loading and unloading times of a shore bridge and a field bridge and the distance between transportation nodes, and taking the lowest transportation cost as an optimization target to construct an unmanned truck dispatching model;
in the present invention, the objective function Z:
Figure BDA0003135778780000041
according to the operating plan, the number of times of full-load driving of the unmanned truck and the number of times of loading and unloading of the shore bridge and the yard bridge are known and fixed, i.e., ∑a,b∈A∑e∈E((Dae/vh)Qae+(Dbe/vh)Qeb)EhAnd (x)ata+xbtb)EwIt has been determined that,
the original objective function Z can be simplified to minimize the total time of empty running of the unmanned truck: see formula (1-1):
Figure BDA0003135778780000042
wherein A represents a container ship set; e represents a container import yard box area set; def、Dae、Dbf、DbaRepresenting the distances between nodes e and f, nodes a and e, nodes b and f, and nodes b and a; v. ofdRepresenting the speed of the unmanned truck when empty; qefRepresenting the number of no-load transportation operation times of the unmanned container truck from the inlet container area e to the outlet container area f; qeaRepresenting the number of times of no-load transportation operation of the unmanned container truck from the inlet box area e to the port-entering ship a; qbfRepresenting the number of times of no-load transportation operation of the unmanned truck from the ship b at the port to the box area f at the outlet; qbaIndicating unmanned container truck from port bAnd (4) carrying out no-load transportation operation times on the ship a entering the port.
Step two: setting the walking times of the unmanned container truck between the ship entering the port and the ship leaving the port, further simplifying the model, and solving by adopting a dynamic planning algorithm to obtain the dynamic planning data of the operation plan of the unmanned container truck;
the setting is that the walking times between the ship entering the port and the ship leaving the port when the unmanned card is collected is as follows: qbaWhen min (M, N), the objective function Z is simplified to:
Figure BDA0003135778780000043
where M represents the number of containers that the inbound ship a needs to unload and N represents the number of containers in the yard box area that need to be loaded onto the outbound ship b.
The constraint condition of the objective function Z is set as
∑e∈EQae＝M (1-3)，
∑f∈FQfb＝N (1-4)，
M+N＝Q (1-5)；
Formula (1-3) representing that the number of times that the unmanned hub transfers the containers unloaded by the inbound ship a to the inbound box area is equal to the number of containers to be unloaded by the inbound ship; m represents the number of containers to be unloaded by the inbound ship a;
the formula (1-4) represents that the times that the unmanned truck transfers the containers to be loaded to the ship b from the yard exit box area are equal to the number of the containers to be loaded by the ship b; n represents the number of containers in the yard box area that need to be loaded onto the ship b at the port of departure;
the formula (1-5) represents that the total container amount of the unmanned truck transportation operation is the sum of the number of containers to be unloaded by the ship a entering the port and the number of containers to be loaded by the ship b leaving the port; q represents the total bin volume for the unmanned truck transport operation.
When the dynamic programming algorithm obtains dynamic programming data, the specific steps are as follows:
(1) setting an operation plan of the unmanned collecting card, wherein n operation tasks are completed in sequenceOr n stages, each stage can be used as the process that an unmanned truck positioned at a certain position in a port firstly drives to a task starting point and then drives to a task ending point with heavy load, and the state variable s is recordedkFor the distribution state of the unmanned collective card at the beginning of the k-stage task, a variable x is decidedkTo complete the unmanned set of k-phase tasks, the decision set is Vk，k＝1,2,3,...,n；
(2) When k phase state variable skAfter the value of the key point is determined, according to a state transfer equation, namely a formula (1-6), the distribution state of the unmanned collecting card after the k-stage task is completed; is given by the formula
Figure BDA0003135778780000051
Wherein
Figure BDA0003135778780000052
Representing a k-phase state transition; sk+1Representing the distribution state of the unmanned card collection after the task of the k stage is completed;
(3) and recording the time of the unmanned truck driving to the task point as a k-stage effect function (1-7) to obtain an objective function formula (1-8), wherein the formula is
Figure BDA0003135778780000053
Figure BDA0003135778780000054
Wherein v iskRepresenting the phase effect of the k phase, namely the empty driving time; diRepresenting the distance between nodes, i representing an empty driving node pair, and i belonging to { ae, ef, bf }; v. ofdRepresenting the speed of the unmanned truck when empty;
(4) obtaining basic equations (1-9) of dynamic planning in an operation plan, and performing n-stage operation according to the dynamically planned data, wherein the equations are as follows:
Figure BDA0003135778780000055
wherein s is0Representing an initial state variable; f. of0(s0) Representing the value of the objective function under the conditions of the initial state variables.
Step three: and checking and optimizing for multiple times through actual operation to obtain the optimal route of the unmanned card concentrator.
Example 1
The invention takes an automatic wharf of a certain port as an example, selects part of a stock dump of the automatic wharf, adopts an algorithm of model construction, model simplification and dynamic programming to carry out example verification, wherein as shown in figure 1, the invention mainly comprises 8 inlet box areas and 4 outlet box areas for container stockpiling. Suppose that at some point in time the inbound ship a and the outbound ship B arrive at the port at the same time. The distances between the vessel and the respective tanks are shown in table 1.
TABLE 1 distance between ship and tank (unit: m)
A B 1A 1B 1C 1D 1E 1F 2A 2B 2C 2D 2E 2F
A
0 300 250 300 350 400 450 500 550 600 650 700 750 800
B 300 0 550 600 650 700 750 800 250 300 350 400 450 500
1A 250 550 0 50 100 150 200 250 300 350 400 450 500 550
1B 300 600 50 0 50 100 150 200 350 300 350 400 450 500
1C 350 650 100 50 0 50 100 150 400 350 300 350 400 450
1D 400 700 150 100 50 0 50 100 450 400 350 300 350 400
1E 450 750 200 150 100 50 0 50 500 450 400 350 300 350
1F 500 800 250 200 150 100 50 0 550 500 450 400 350 300
2A 550 250 300 350 400 450 500 550 0 50 100 150 200 250
2B 600 300 350 300 350 400 450 500 50 0 50 100 150 200
2C 650 350 400 350 300 350 400 450 100 50 0 50 100 150
2D 700 400 450 400 350 300 350 400 150 100 50 0 50 100
2E 750 450 500 450 400 350 300 350 200 150 100 50 0 50
2F 800 500 550 500 450 400 350 300 250 200 150 100 50 0
The shore bridge and the field bridge can only load and unload one container at a time, and the unmanned truck can only transport one container at a time. 6 unmanned trucks in the port carry out horizontal transportation operation and are initially distributed on the bank and the inlet and outlet box areas, and the speed v of the unmanned trucks during heavy loadh4m/s, speed v at no loaddThe average working time for loading and unloading one container by the shore bridge is 120s, and the average working time for loading and unloading one container by the field bridge is 90 s. According to the operation plan, the inbound ship a needs to perform 38 container ship unloads, and the outbound ship B needs to perform 12 container ship unloads, and the detailed plan is shown in table 2.
TABLE 2 unmanned truck-collecting operation schedule
Figure BDA0003135778780000061
Figure BDA0003135778780000071
According to the data, a dynamic programming algorithm is adopted to solve the unmanned card collecting scheduling model, and a Python language is applied to programming, so that the optimal route of the unmanned card collecting is obtained as shown in the following table 3.
TABLE 3 unmanned truck haul route
Figure BDA0003135778780000072
Figure BDA0003135778780000081
According to the unmanned truck-collecting transportation path, the empty load rate of the whole operation process is 38.87% through calculation, the empty load rate of truck-collecting dispatching operation in the traditional operation line mode is about 50%, the empty load rate is reduced remarkably, the operation efficiency of the automatic wharf can be improved remarkably, and the operation cost of the port is reduced.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (5)

1. An unmanned truck-concentration scheduling optimization method between a multi-type intermodal ship and a yard is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: according to an operation plan, combining the full-load driving times of the unmanned truck, the loading and unloading times of a shore bridge and a field bridge and the distance between transportation nodes, and taking the lowest transportation cost as an optimization target to construct an unmanned truck dispatching model;
step two: setting the walking times of the unmanned container truck between the ship entering the port and the ship leaving the port, further simplifying the model, and solving by adopting a dynamic planning algorithm to obtain the dynamic planning data of the operation plan of the unmanned container truck;
step three: and checking and optimizing for multiple times through actual operation to obtain the optimal route of the unmanned card concentrator.
2. The unmanned truck-concentration scheduling optimization method between the multi-type intermodal ship and the yard according to claim 1, characterized by comprising the following steps: in the first step, the lowest transportation cost is taken as an optimization target, an unmanned truck dispatching model is constructed, and the shortest total time of an unmanned truck no-load mode is set as a target function Z, which is specifically shown in a formula (1-1):
Figure FDA0003135778770000011
wherein A represents a container ship set; e represents a container import yard box area set; def、Dae、Dbf、DbaRepresenting the distances between nodes e and f, nodes a and e, nodes b and f, and nodes b and a; v. ofdRepresenting the speed of the unmanned truck when empty; qefRepresenting the number of no-load transportation operation times of the unmanned container truck from the inlet container area e to the outlet container area f; qeaRepresenting the number of times of no-load transportation operation of the unmanned container truck from the inlet box area e to the port-entering ship a; qbfRepresenting the number of times of no-load transportation operation of the unmanned truck from the ship b at the port to the box area f at the outlet; qbaIndicating the number of times the unmanned truck is unloaded from the ship b to the ship a.
3. The unmanned truck-concentration scheduling optimization method between the multi-type intermodal ship and the yard according to claim 2, characterized by comprising the following steps: in the second step, when the model is simplified, the specific method is as follows:
the setting is that the walking times between the ship entering the port and the ship leaving the port when the unmanned card is collected is as follows: qbaWhen min (M, N), the objective function Z is simplified to:
Figure FDA0003135778770000012
where M represents the number of containers that the inbound ship a needs to unload and N represents the number of containers in the yard box area that need to be loaded onto the outbound ship b.
4. The unmanned truck-concentration scheduling optimization method between the multi-type intermodal ship and the yard according to claim 3, characterized by comprising the following steps: the constraint condition of the objective function Z is set as
∑e∈EQae＝M (1-3)，
∑f∈FQfb＝N (1-4)，
M+N＝Q (1-5)；
Formula (1-3) representing that the number of times that the unmanned hub transfers the containers unloaded by the inbound ship a to the inbound box area is equal to the number of containers to be unloaded by the inbound ship; m represents the number of containers to be unloaded by the inbound ship a;
the formula (1-4) represents that the times that the unmanned truck transfers the containers to be loaded to the ship b from the yard exit box area are equal to the number of the containers to be loaded by the ship b; n represents the number of containers in the yard box area that need to be loaded onto the ship b at the port of departure;
the formula (1-5) represents that the total container amount of the unmanned truck transportation operation is the sum of the number of containers to be unloaded by the ship a entering the port and the number of containers to be loaded by the ship b leaving the port; q represents the total bin volume for the unmanned truck transport operation.
5. The unmanned truck-concentration scheduling optimization method between the multi-type intermodal ship and the yard according to claim 3, characterized by comprising the following steps: in the second step, when the dynamic programming algorithm obtains dynamic programming data, the specific steps are as follows:
(1) setting the operation plan of the unmanned collecting card, which is to complete n operation tasks or n stages in sequence and record the state variable skFor the distribution state of the unmanned collective card at the beginning of the k-stage task, a variable x is decidedkTo complete the unmanned set of k-phase tasks, the decision set is Vk，k＝1,2,3,...,n；
(2) When k phase state variable skAfter the value of the key point is determined, according to a state transfer equation, namely a formula (1-6), the distribution state of the unmanned collecting card after the k-stage task is completed; is given by the formula
Figure FDA0003135778770000021
Wherein
Figure FDA0003135778770000022
Representing a k-phase state transition; sk+1Representing the distribution state of the unmanned card collection after the task of the k stage is completed;
(3) and recording the time of the unmanned truck driving to the task point as a k-stage effect function (1-7) to obtain an objective function formula (1-8), wherein the formula is
Figure FDA0003135778770000023
Figure FDA0003135778770000024
Wherein v iskRepresenting the phase effect of the k phase, namely the empty driving time; diRepresenting the distance between nodes, i representing an empty driving node pair, and i belonging to { ae, ef, bf }; v. ofdRepresenting the speed of the unmanned truck when empty;
(4) obtaining basic equations (1-9) of dynamic planning in an operation plan, and performing n-stage operation according to the dynamically planned data, wherein the equations are as follows:
Figure FDA0003135778770000025
wherein s is0Representing an initial state variable; f. of0(s0) Representing the value of the objective function under the conditions of the initial state variables.
CN202110718074.9A 2021-06-28 2021-06-28 Multi-type intermodal ship and yard unmanned truck-concentration scheduling optimization method Pending CN113627643A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110718074.9A CN113627643A (en) 2021-06-28 2021-06-28 Multi-type intermodal ship and yard unmanned truck-concentration scheduling optimization method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110718074.9A CN113627643A (en) 2021-06-28 2021-06-28 Multi-type intermodal ship and yard unmanned truck-concentration scheduling optimization method

Publications (1)

Publication Number Publication Date
CN113627643A true CN113627643A (en) 2021-11-09

Family

ID=78378463

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110718074.9A Pending CN113627643A (en) 2021-06-28 2021-06-28 Multi-type intermodal ship and yard unmanned truck-concentration scheduling optimization method

Country Status (1)

Country Link
CN (1) CN113627643A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115271615A (en) * 2022-09-19 2022-11-01 质子汽车科技有限公司 Cross-border transportation method and device, electronic equipment and storage medium
CN116205470A (en) * 2023-05-05 2023-06-02 中铁第四勘察设计院集团有限公司 Container synchronous transfer scheduling optimization method and system
CN116258063A (en) * 2023-01-06 2023-06-13 广州港集团有限公司 Port operation optimal path planning simulation method and system based on genetic algorithm

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115271615A (en) * 2022-09-19 2022-11-01 质子汽车科技有限公司 Cross-border transportation method and device, electronic equipment and storage medium
CN116258063A (en) * 2023-01-06 2023-06-13 广州港集团有限公司 Port operation optimal path planning simulation method and system based on genetic algorithm
CN116205470A (en) * 2023-05-05 2023-06-02 中铁第四勘察设计院集团有限公司 Container synchronous transfer scheduling optimization method and system
CN116205470B (en) * 2023-05-05 2023-08-04 中铁第四勘察设计院集团有限公司 Container synchronous transfer scheduling optimization method and system

Similar Documents

Publication Publication Date Title
CN113627643A (en) Multi-type intermodal ship and yard unmanned truck-concentration scheduling optimization method
Kizilay et al. A comprehensive review of quay crane scheduling, yard operations and integrations thereof in container terminals
CN113361804B (en) Buffer yard operation and inter-dock truck transportation cooperative scheduling system and method thereof
Kim et al. Deriving decision rules to locate export containers in container yards
Tang et al. Modeling and solution of the joint quay crane and truck scheduling problem
CN101585453B (en) Distribution Method for export container yard of container wharf
CN111861205B (en) Automatic container wharf outlet box space distribution method
Hu et al. Improved Benders decomposition for stochastic yard template planning in container terminals
CN113298403B (en) Public molten iron intermodal transportation scheduling method based on transportation time window synchronization
Ioannou Introduction to Intelligent Freight Transportation
WO2019116933A1 (en) Container terminal system utilizing artificial intelligence
CN110942203A (en) Automatic container terminal AGV path optimization method
Ambrosino et al. Optimization challenges and literature overview in the intermodal rail-sea terminal
CN116629424A (en) Novel automatic double-yard bridge intelligent scheduling method for container yard operation mode
CN113095753B (en) Unmanned integrated card scheduling method based on intelligent management dispatching of containers
Walha et al. A rail-road PI-hub allocation problems: model and heuristic
Hassan et al. Analysis of the container dwell time at container terminal by using simulation modelling
Kim Models and methods for operations in port container terminals
CN110544067B (en) Multi-type combined transport system
CN109872023B (en) Integrated scheduling model of quay bridge QC, integrated card YT and gantry crane YC and GA algorithm thereof
Hu et al. A review of intermodal rail freight bundling operations
CN116205470B (en) Container synchronous transfer scheduling optimization method and system
CN117973956A (en) Scheduling method and device
Reefke Simulation of container traffic flows at a metropolitan seaport
CN117787850A (en) AGV vehicle transportation task distribution method and system based on YTS-SAP model

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination