CN113627643A - Multi-type intermodal ship and yard unmanned truck-concentration scheduling optimization method - Google Patents
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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
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):
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:
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
WhereinRepresenting 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
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:
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:
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):
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:
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
WhereinRepresenting 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
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:
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 | | |
| |
|
|
1F | |
| |
2D | |
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
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
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):
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:
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
WhereinRepresenting 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
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:
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.
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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 |
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