CN115983746A - Multi-main-body collaborative optimization operation method for automatic container port - Google Patents
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Abstract
The invention discloses an automatic container port multi-body collaborative optimization operation method, which relates to the field of port scheduling operation.A port operation time shortest model and a port operation cost shortest model are constructed by the method, and assumptions and constraints are made on the models by considering port fixed cost, carbon emission, carbon tax and the like; the performance of the grey wolf algorithm is tested by utilizing the grey wolf algorithm to carry out individual coding, generating an initial population and the like; and finally, calculating by using an example, and verifying the effectiveness of the model and the algorithm. According to the scheme, the mathematical model is used for simulating the actual container port cooperative operation method, relevant factors influencing the container port operation efficiency can be analyzed, the multi-target problem is converted into the single-target problem by surrounding three main bodies, namely a berth, a shore bridge and an AGV in the operation process, the actual problem is convenient to simulate and analyze, meanwhile, the standard deviation of the gray wolf algorithm is lower than that of other two algorithms, and the stability is higher.
Description
Technical Field
The invention relates to the field of port scheduling operation, in particular to an automatic container port multi-main-body collaborative optimization operation method.
Background
Some current research in the related art considers carbon emissions during ship berthing activities, and port operations are rarely considered. In the aspect of solving port operation scheduling, the current common technical means comprises a first-come first-served berth distribution mode, a function is constructed by taking the shortest port time of a ship as a target, and finally, the function is solved through a heuristic algorithm; an optimization model aiming at the shortest port time of the ship is constructed through the ship stage and the ship preference position; the ship is limited through ship berthing and operation, a time window of the ship operation is considered, and a heuristic algorithm is used for solving; still other techniques construct a multi-Agent joint scheduling optimization model to reduce job cost and time.
Most of the current technical means only independently research a certain link, and in the actual port operation process, the operations of different links should be considered in a coordinated manner, and a production operation plan is systematically made, so that the operation cost of the container port is reduced, and the operation efficiency is improved. This patent is on solving harbour operation flow optimization problem, from harbour enterprise operation management's angle, has discussed the carbon dioxide emission activity of each operation equipment in harbour, including loading and unloading transport operation and auxiliary production operation, through the comprehensive optimization to whole operation flow, has compensatied the technical vacancy in relevant field.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
an automatic container port multi-body collaborative optimization operation method comprises the following steps:
s1, constructing a collaborative optimization model of three main bodies, namely a port berth, a shore bridge and an AGV, according to set conditions;
s2, setting a multi-objective function and a constraint condition for the constructed collaborative optimization model by taking the shortest port operation time and the lowest operation cost as objective functions;
s3, setting different scene parameters, coding the constructed model by using a wolf algorithm, respectively calculating the fitness of each objective function under different scene parameters, and performing iterative updating;
and S4, outputting an optimal collaborative optimization operation scheme when the maximum iterative algebra is reached.
Further, the objective function in S2 is represented as:
wherein,
k is the actual departure time of the container ship;
The arrival time of the schedule of the container ship k is shown;
Time costs for container ships at port;
The operation time of the quayside container crane c after being selected by the container ship k;
The operation cost of each shore bridge in unit time is calculated;
The unit time operation cost of a single AGV;
Fixed cost is used for the shore bridge;
Allocating the number of shore bridges for the container ship k;
Fixed costs are used for the AGV;
Distributing the number of AGV for the container ship k;
The delay operation time of waiting for the r-th AGV of the shore bridge c selected for the container ship k;
The cost of the shore bridge delay operation is saved;
Waiting for the delay operation time of a shore bridge c by an r-th AGV selected for the container ship k;
Delaying the operation cost for a single AGV;
The latest departure time of the container ship k;
Delay operation cost for container ships;
Carbon emission per unit time for container ships at portTax costs;
K is a carbon emission coefficient of the container ship in unit time;
The time when the container ship k actually arrives at the berth;
The use cost of shore power is reduced;
Is a port berth shore bridge set;
Gathering a container terminal sea AGV;
The ship set for loading and unloading operation at the port within a time period;
The port operation cost is the lowest objective function;
The method is an objective function for minimizing port operation time.
Further, the specific manner of setting the objective function in S2 is as follows:
converting the multi-target problem into a single-target planning model in a linear weighting mode, wherein the single-target planning model is expressed as follows:
wherein,
and &>
Is the weight coefficient of each objective function.
Further, the constraint conditions in S2 include:
(1) The berthing positions and berthing time of the container ships are not overlapped;
(2) When the boxed ship is berthed, the requirement that the ship length is smaller than the length of an idle shoreline is met;
(3) The container ship arrives at the berth later than the port;
(4) The real departure time of the container ship is equal to the sum of the time of the container ship reaching the berth and the time of the operation at the berth;
(5) The time for finishing the operation of the last shore bridge is the time for the container ship to operate at the berth, and the container ship is released after the shore bridge operation is finished;
(6) Each shore bridge can only serve one container ship at the same time;
(7) The number of the shore bridges selected by the container ship cannot be larger than the total number of the shore bridges of the port;
(8) The number of the AGV selected by the container ship cannot be larger than the total number of the shore bridges of the port;
(9) The sum of the work task amount allocated to each shore bridge is equal to the total task amount of the container ship;
(10) The number of AGVs served cannot exceed the total number of devices in the terminal;
(11) The number of AGV's operating at any moment is equal to the number of ships assigned to the container;
(12) The operation time of the shore bridge c and the container ship k service meet the time relation:
wherein,
the operation time of the quay crane c is set;
Selecting a shore bridge with the minimum workload from the shore bridges c for the container ship k;
The operation efficiency of loading containers for the quayside container crane in a double-pass manner is improved;
Selecting a shore bridge with the largest workload from shore bridges c for the container ship k;
The operation efficiency of loading containers for one way of the shore bridge is improved;
Allocating the task amount of containers needing to be loaded on the ship to the container ship k by the shore bridge c;
The amount of container tasks that the shore bridge c assigned to the container ship k needs to unload from the ship;
(13) The delay operation time of waiting for the r-th AGV of the quayside container carrier c selected by the container ship k meets the time relation:
wherein,
the operation efficiency of loading the containers for the AGV in one way;
The operation efficiency of loading the containers for the AGV in a double-pass manner;
(14) The delay operation time of the r AGV selected by the container ship k for waiting the shore bridge c meets the time relation:
further, the S3 specifically includes:
s31, setting basic parameters of the gray wolf algorithm, including the size of a population, the iteration times and the weight coefficient of an objective function;
s32, coding the collaborative optimization model, and randomly generating an initialization population of the container port operation process;
s33, fitness calculation is carried out on the initialization population generated randomly, and the optimal individual in the current population is selected;
and S34, performing loop iteration by using the selected optimal individuals in the current population to obtain a fitness function curve of the objective function with the lowest port operation cost and the objective function with the lowest port operation time, and solving a cooperative operation scheme comprising a ship berthing position, the quantity of distributed shore bridges and AGV and operation time according to the obtained fitness function curve.
This scheme has following beneficial effect:
1. the actual container port cooperative operation method is simulated by using the mathematical model, so that the relevant factors influencing the container port operation efficiency can be analyzed, and the multi-target problem is converted into the single-target problem by surrounding the cooperative optimization among the three main bodies, namely the berth, the shore bridge and the AGV in the operation flow, so that the actual problem can be simulated and analyzed conveniently.
2. The optimal value and the average value of the wolf algorithm are obviously lower than those of the particle swarm algorithm and the genetic algorithm, and the precision of the wolf algorithm in the multi-subject collaborative optimization of the container port is higher. Meanwhile, the standard deviation of the method is lower than that of other two algorithms, and the method has higher stability.
Drawings
Fig. 1 is a schematic flow chart of a multi-body collaborative optimization operation method for an automated container port.
Fig. 2 is a diagram illustrating operation results of the benchmark test function according to an embodiment of the present invention, where (a) is a Sphere operation result, (b) is a Schwefel operation result, (c) is a rastigin operation result, (d) is an Ackley operation result, (e) is a kowailk operation result, and (f) is a hekel operation result.
FIG. 3 is a diagram illustrating operation results of different algorithms according to an embodiment of the present invention.
FIG. 4 is a graph of fitness function of an objective function according to an embodiment of the present invention, wherein (a) is minimum cost and (b) is minimum time.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
An automatic container port multi-subject collaborative optimization operation method is shown in fig. 1, and comprises the following steps:
s1, constructing a collaborative optimization model of three main bodies, namely a port berth, a shore bridge and an AGV, according to set conditions;
in this embodiment, the collaborative optimization model includes the following:
parking space
When the ships are queued at anchor places, the normal operation of the ships needs to be maintained, fuel oil power generation is needed to be assisted, carbon emission generated by fuel oil combustion power generation is considered as carbon emission generated by a port side, and the carbon tax is collected to the port side;
the container ships arriving at port are all provided with shore power equipment and can use shore power at berth, no subsidy is provided for the ship using shore power expense and the port cost is only the shore power construction cost and the maintenance cost;
the shore line is a straight line and is a continuous berth, and when the container ships berth, the safe distance between the container ships needs to be considered, and the part is known from the exterior and interior of the ship period;
the influence of special events, such as the influence of tidal depth on berthing, equipment maintenance, weather and the like, is not considered;
the arrival time of the ship schedule is used as the arrival time of the ship, and the penalty cost is calculated when the departure time of the ship schedule exceeds the arrival time;
the moving speed of the ship from an anchoring ground to a berth is the same, and the berthing situation does not occur until the loading and unloading operation is finished after the ship arrives at the berth;
the operation time of the container ship at the port is approximately equal to the loading and unloading operation time of the shore bridge and the AGV, namely the shore bridge operation is finished, and the container ship leaves the berth to reserve a space for the next ship to enter the berth;
shore bridge
The quantity of the shore bridges is fixed, the shore bridges can move through the rails, and the quantity of the shore bridges is distributed according to the length of the container and the loading and unloading workload;
the port is a full-automatic container port, manual operation is not performed, the moving speed and the operation efficiency of the shore bridge are constant, the influence of external factors is avoided, and the time difference of container operation caused by different positions in the container is ignored;
the quay crane can only serve the appointed container ship at the same time, and once the container ship operation is completed, the quay crane is released to serve other ships;
a certain safe operation space is reserved between the shore bridges;
the moving time of the shore bridge is smaller than the whole operation time, so that the shore bridge can be distributed in a crossing way when the shore bridge is distributed to the container ship, and the time cost is not calculated;
AGV
the AGV moving speed is constant, and once the corresponding operation area is allocated, cross-area operation can be performed only after the operation is completely finished;
AGV charging time is not considered;
the transverse span of the port yard is large, and the AGV operation efficiency is averaged, so that the difference of the efficiency of different yards is not considered;
AGV and bank bridge adopt the electric power energy supply, do not all count into carbon and discharge.
Aiming at the collaborative optimization model, the following relevant symbols are set for subsequent calculation:
(1) Container terminal resources
G is a maximum value;
(2) Information related to a schedule
(3) Equipment operating efficiency and cost:
(4) Decision variables
(5) Dependent variable
S2, setting a multi-objective function and a constraint condition for the constructed collaborative optimization model by taking the shortest port operation time and the lowest operation cost as objective functions;
the model constructs a double-target optimization model with the lowest cost of the container ship at the port and the shortest total time at the port from the view point of the port side.
(1) Port cost minimum objective function
Aiming at the lowest cost of the container ship in port, the cost is divided into the following parts: 1. the cost of container ships in port time, 2, the cost of container ships in port production operation, 3, the cost of container ships delayed operation, and 4, carbon tax and shore power cost.
Container ship on port time cost
Container ships in ports are mainly composed of the following stages: and (4) waiting for anchoring ground, enabling the anchoring ground to arrive at the berth, loading and unloading the berth, and leaving the berth. Since the container ship can use shore power only during berthing operations, the container ship is divided into two parts at port time: the first is the queuing time, i.e. the time from the arrival of the container ship at the anchoring site to the time when the ship arrives at the berth to start the operation, and the second is the loading and unloading operation time, i.e. the time from the arrival of the container ship at the berth to the time when the operation is finished and the container ship leaves the port.
Anchor queuing time:
loading and unloading operation time:
the total time cost of the ship in port is as follows:
2) Production operation cost of container port
The production operation cost comprises operation time cost and fixed equipment use cost, wherein the operation time cost refers to the operation time of the shore bridge and the AGV multiplied by the operation cost in unit time and is respectively
. The equipment use also needs certain fixed cost, and the main structure is that the quantity of the selected equipment is multiplied by the fixed depreciation maintenance cost of the equipment. In which the loading and unloading times of a quay crane are based on the number of containers k loaded and unloaded>
、
Number of shore bridges allocated to the container ship k->
AGV number>
And the equipment operation efficiency is determined together.
To sum up, the operation cost of the container port is as follows:
3) Delay operation cost of container ship
Two delay conditions mainly exist in container ports, namely, a container ship cannot be berthed in time according to a ship schedule, and limited resources of the ports cannot be met in time when the number of ships is too large and anchored ground is queued or the loading and unloading amount of the ships is too large, so that delay is caused. We assume here that all container ships arrive at port according to the schedule of arrival times, only considering delays in the ships due to queuing and handling operations. Therefore, the delayed operation cost is as follows:
4) Carbon taxes and shore power plant costs
Compared with the traditional container port operation equipment, all the operation equipment of the port adopts electric power for energy supply, and the carbon emission of the port operation equipment is not considered, and only the carbon emission of container ships is considered. The cost of carbon tax is the period from the arrival of the container ship at the anchor to the departure of the container ship from the port, and the cost of the part is divided into two parts, namely the cost of carbon tax of the auxiliary motor for supplying the fuel at the anchor of the container ship, and the cost of shore power for berthing the container ship, wherein the main cost of the port is the cost of construction and maintenance of the shore power for each container.
The carbon tax of container ship berthing anchor land is:
cost of shore power when the container ship is berthed:
the four parts are summarized as the objective function with the lowest cost:
(2) Shortest time objective function
In the formula,
indicating the cost of the container ship at port time,
represents the operation cost of the container ship on the berth shore bridge and the AGV>
Represents a delay cost for the shore bridge waiting for the AGV, < >>
Represents the latency cost of the AGV waiting on the shore bridge, < >>
Representing the delay cost of a container ship not departing on time. The number of the added containers mainly takes the delay cost of different ships and the loading and unloading of the containers into considerationThe number of bins is a matter of course.
(3) Multi-objective model
The method solves the multi-target problem by adopting a weight value conversion method. Converting the multi-target problem into a single-target planning model in a linear weighting mode:
wherein
And &>
Satisfies ^ H for the weight coefficient of each target>
. The two objective functions are cost and time, respectively. Considering two different working scenes in actual operation, the working scene with the lowest working cost and the shortest working time are considered, and the scene with the lowest working cost is considered>
(ii) a Scene with shortest operation time>
。
Constraint conditions are as follows:
the following equation 3 indicates that the berthing positions and berthing times of container ships cannot overlap, that is, the same berth can only serve one container ship at the same time:
the following formula represents that the length of the container ship is less than the length of an idle shoreline when the container ship is berthed:
the following equation represents the container ship arriving at the berth later than the port:
the following equation represents the true departure time of a container ship equal to the time the container ship arrives at berth plus the time at berth for the operation:
the time when the last shore bridge finishes the operation is represented by the following formula, namely the time when the container ship operates at the berth, and the container ship is released after the shore bridge operation is finished:
the following equation 3 represents the relationship between the working time of the shore bridge c and the service time of the container ship k:
the following equation indicates that each quay crane can only serve one container ship at a time:
the following formula represents the constraint of the number of the shore bridges, namely the total number of the shore bridges which are selected by the container ship and can not be larger than the number of the shore bridges of a large port:
the same applies to the constraint of AGVs, i.e. the number of AGVs selected for a container ship cannot be greater than the total number of shore bridges in a port:
the following equation represents the constraint on the number of container ship service shore bridges, i.e. the number of allocated shore bridges required to meet the maximum and minimum numbers:
the workload constraint is expressed by the following equation, i.e. the sum of the workload assigned to each shore bridge equals the total workload of the container ship:
the number constraint of AGVs, i.e. the number of AGVs serviced cannot exceed the total number of devices in the terminal, is expressed by:
the following equation indicates that at any time, the number of AGVs for a job equals the number of assigned container ships, which constraint requires that the AGVs cannot be released before the container job is completed:
the following formula represents the operation time of the shore bridge, and the shore bridge operating in the same way cannot work for other ships before the container ship finishes the operation:
the time that the shore bridge waits for the AGV is expressed as follows:
the following equation represents the time for the AGV to wait on the shore bridge:
s3, setting different scene parameters, coding the constructed model by utilizing a wolf algorithm, respectively calculating the fitness of each objective function under the different scene parameters, and performing iterative updating;
in this embodiment, in order to compare the advantages and disadvantages of the algorithms, the population size is set to 50, and the maximum number of iterations is 50. The main setting parameters of the genetic algorithm are cross probability and variation probability, wherein the cross probability is usually 0.4 to 0.99, and the variation probability is usually 0.0001 to 0.1. Too low a cross-over probability is not favorable for updating the population, here set to 0.9. Too small variation probability can cause too fast reduction of diversity of the population, which is not beneficial to solving the optimal individual, and the variation probability is selected to be 0.1. The particle swarm algorithm mainly sets parameters including inertia weight, speed range and learning constant. The typical inertia coefficient is 0.7 to 0.9, and is set to 0.8, the speed range is 0.5, and the learning constant is 1. The gray wolf algorithm is simple in structure and needs to set the coefficient vector to be 1. When evaluating the performance of an algorithm, a benchmark function is usually used, and the benchmark function is a standard special function used for testing the optimizing performance of the algorithm. Two test functions are selected for each type of the three types of the unimodal test function, the multimodal test function and the fixed-dimension multimodal test function to be tested.
The operation is performed for 50 times on each benchmark test function, the output of each operation of the three types of benchmark test functions under different algorithm operations is obtained as shown in fig. 2, the abscissa represents the operation times, the ordinate represents the adaptability value of each operation, and the operation is performed for 50 times in total. From the operation result, the optimal value and the average value of the adaptability values of the wolf algorithm and the particle swarm algorithm are closer to the theoretical optimal value compared with the genetic algorithm.
The grey wolf algorithm solving process comprises the following steps:
s31, setting basic parameters of the gray wolf algorithm, including the population size, the number of iterations, and the weight coefficient of the objective function, in this embodiment, in order to obtain a better optimization scheme, and within an acceptable solution time range, the following values are selected as the basic parameters of the gray wolf algorithm: the population size is 100, the maximum iteration frequency is 500, the objective function adopts a linear weighting combination method, the importance degree of each objective under different scenes is respectively calculated and multiplied by the corresponding weight coefficient, and the operation cost is minimum under the scene
(ii) a Under the scene with shortest working time>
Thereby constituting an objective function.
S32, coding the collaborative optimization model, and randomly generating an initialization population of a container port operation process, specifically, initializing container port berths, a shore bridge and an AGV, and randomly generating the initialization population, wherein the first part is the arrangement sequence of container ships; the second part is a container ship berthing position, the central point of the ship is taken as the berthing position, and the initial berthing position is randomly generated; the third part represents the number of the shore bridges, the numerical value of the number of the shore bridges is between the minimum number of the shore bridges and the maximum number of the shore bridges which can be carried by the container ship, and the moving time of the shore bridges is considered to be negligible relative to the operation time, so that the distribution positions and the moving time of the shore bridges are not considered into a model; the fourth part represents the number of AGVs, and the sum of the number of all the AGVs in the service state is required to be less than the sum of the number of the AGVs
S33, fitness calculation is carried out on the initialized population generated randomly, the optimal individual in the current population is selected by carrying out fitness calculation on the initialized container port operation related process, and the optimal individual is generated by continuous loop iteration.
And S34, performing loop iteration by using the selected optimal individuals in the current population to obtain a fitness function curve of the objective function with the lowest port operation cost and the objective function with the lowest port operation time, as shown in FIG. 4, obtaining a cooperative operation scheme by using the obtained fitness function curve, obtaining a ship berthing position by iteration, and distributing the quantity of shore bridges and AGVs, operation time and other results.
And S4, outputting an optimal collaborative optimization operation scheme when the maximum iterative algebra is reached.
And solving and verifying effectiveness.
The method analyzes two situations, namely, different operation strategies are adopted for the same ship in the port, namely, under the same arrival intensity; one is the allocation of port operations equipment under the same strategy and different arrival strengths. In order to control variables, later, when multiple container port main bodies cooperate in different scenes, the ports need to select different operation strategies for equipment allocation, the same arrival intensity needs to be controlled, so as to judge the influence of the different strategies on the allocation of the port operation equipment with the arrival intensity, and λ =1.16 is used as a case for analysis, namely 28 arriving ships in the operation period are used as cases; and when the equipment distribution under different arrival strengths is compared, three groups of data are selected for comparative analysis. In order to test the performance of the grayish wolf algorithm in the multi-subject collaborative optimization of the container port, based on the parameter setting, the grayish wolf algorithm, the particle swarm algorithm and the genetic algorithm are respectively used for solving the multi-subject collaborative optimization model of the port mentioned in section three. In order to conveniently compare the performances of the algorithms in the port multi-subject collaborative optimization, the solution is carried out by taking the minimum total cost of the container port in port operation as a target. The test was performed with λ =1.16, i.e. 28 ships arriving at port in the operation cycle, where the population size was set to 50, and the number of iterations was set to 100, because the calculation amount was too large, ten experiments were performed.
The fitness values of the different algorithms obtained by the ten operations through solving are shown in fig. 3. The abscissa represents the number of operations, and the ordinate represents the fitness value output for each operation. In order to compare the advantages and disadvantages of the algorithms more visually, the optimal value, the average value and the standard deviation of results for ten times are selected for auxiliary judgment. The optimal value and the average value of the wolf algorithm are obviously lower than those of the particle swarm algorithm and the genetic algorithm, and the precision of the wolf algorithm in the multi-subject collaborative optimization of the container port is higher. Meanwhile, the standard deviation of the method is lower than that of the other two algorithms, which shows that the gray wolf algorithm has higher stability.
After calculation and circulation for 500 times, the fitness function curve graphs under two scenes of the minimum cost and the minimum time are obtained by setting the weight of the cost and the time in the objective function, as shown in fig. 4.
Based on the process, the berthing position of the ship is obtained through iteration, and results such as the number of shore bridges and AGV are distributed, the operation time and the like are obtained.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (5)
1. An automatic container port multi-body collaborative optimization operation method is characterized by comprising the following steps:
s1, constructing a collaborative optimization model of three main bodies, namely a port berth, a shore bridge and an AGV, according to set conditions;
s2, setting a multi-objective function and a constraint condition for the constructed collaborative optimization model by taking the shortest port operation time and the lowest operation cost as objective functions;
s3, setting different scene parameters, coding the constructed model by utilizing a wolf algorithm, respectively calculating the fitness of the objective function under the different scene parameters and carrying out iterative updating;
and S4, outputting an optimal collaborative optimization operation scheme when the maximum iterative algebra is reached.
2. The method as claimed in claim 1, wherein the multi-objective function in S2 is expressed as:
wherein,
k is the actual departure time of the container ship;
K, the arrival time of the schedule of the ship time for the container ship;
Time costs for container ships at port;
The operation time after the quay crane c is selected by the container ship k;
The operation cost of each shore bridge in unit time is calculated;
The unit time operation cost of a single AGV;
Fixed cost is used for the shore bridge;
Allocating the number of shore bridges for the container ship k;
Fixed costs are used for the AGV;
Distributing the number of AGV for the container ship k;
The delay operation time of waiting for the r-th AGV of the shore bridge c selected for the container ship k is calculated;
The cost of the shore bridge delay operation is saved;
Waiting for the delay operation time of a shore bridge c by an r-th AGV selected for the container ship k;
Delaying the operation cost for a single AGV;
The latest departure time of the container ship k;
Delay operation cost for container ships;
Carbon tax costs per unit time of carbon emissions for container ships at port;
K is a carbon emission coefficient of the container ship in unit time;
The time when the container ship k actually arrives at the berth;
The use cost of shore power is reduced;
Is a port berth shore bridge set;
Gathering a container terminal sea AGV;
The ship assembly is a ship assembly for loading and unloading operation at a port within a time period;
The port operation cost is the lowest objective function;
The port operation time is the minimum objective function.
3. The method as claimed in claim 1, wherein the specific way of setting the multi-objective function in S2 is as follows:
converting the multi-target problem into a single-target planning model in a linear weighting mode, wherein the single-target planning model is expressed as follows:
wherein,
and &>
And Z is the port operation cost and T is the port operation time.
4. The method as claimed in claim 1, wherein the constraint conditions in S2 include:
(1) The berthing positions and berthing time of the container ships are not overlapped;
(2) When the boxed ship is berthed, the requirement that the ship length is smaller than the length of an idle shoreline is met;
(3) The container ship arrives at the berth later than the port;
(4) The real departure time of the container ship is equal to the sum of the time of the container ship reaching the berth and the time of the operation at the berth;
(5) The time for finishing the operation of the last shore bridge is the time for the container ship to operate at the berth, and the container ship is released after the shore bridge operation is finished;
(6) Each shore bridge can only serve one container ship at the same time;
(7) The number of the shore bridges selected by the container ship cannot be larger than the total number of the shore bridges of the port;
(8) The number of the AGV selected by the container ship cannot be larger than the total number of the shore bridges of the port;
(9) The sum of the work task amount allocated to each shore bridge is equal to the total task amount of the container ship;
(10) The number of AGVs served cannot exceed the total number of devices in the terminal;
(11) The number of AGV's operating at any moment is equal to the number allocated to the container ships;
(12) The operation time of the shore bridge c and the container ship k service meet the time relation:
wherein,
the operation time of the quay crane c is set;
Selecting a shore bridge with the minimum workload in the shore bridges c for the container ship k;
the operation efficiency of loading containers for the quayside container crane in a double-pass manner is improved;
Selecting a shore bridge with the largest workload from shore bridges c for the container ship k;
The operation efficiency of loading containers for one way of the shore bridge is improved;
Allocating the task amount of containers needing to be loaded on the ship to the container ship k by the shore bridge c;
The amount of container tasks that the shore bridge c assigned to the container ship k needs to unload from the ship;
(13) The delay operation time of waiting for the r-th AGV of the shore bridge c selected by the container ship k meets the time relation:
wherein,
the operation efficiency of loading the containers for the AGV in one way;
The operation efficiency of loading containers for AGV two-way;
(14) The delay operation time of the r AGV selected by the container ship k for waiting the shore bridge c meets the time relation:
5. the method as claimed in claim 1, wherein the step S3 specifically comprises:
s31, setting basic parameters of the gray wolf algorithm, including the size of a population, the iteration times and the weight coefficient of an objective function;
s32, coding the collaborative optimization model, and randomly generating an initialization population of the container port operation process;
s33, fitness calculation is carried out on the initialized population generated randomly, and the optimal individual in the current population is selected;
and S34, performing loop iteration by using the selected optimal individuals in the current population to obtain a fitness function curve of the objective function with the lowest port operation cost and the objective function with the lowest port operation time, and obtaining a cooperative operation scheme comprising ship berthing positions, the quantity of allocated shore bridges and AGVs and operation time according to the obtained fitness function curve.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117035338A (en) * | 2023-08-18 | 2023-11-10 | 无锡鲸云信息科技有限公司 | Port production operation real-time monitoring method and system |
| CN117474297A (en) * | 2023-12-27 | 2024-01-30 | 南京信息工程大学 | Optimization method for ship berth and quay crane distribution for automatic wharf |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117035338A (en) * | 2023-08-18 | 2023-11-10 | 无锡鲸云信息科技有限公司 | Port production operation real-time monitoring method and system |
| CN117035338B (en) * | 2023-08-18 | 2024-02-02 | 无锡鲸云信息科技有限公司 | Port production operation real-time monitoring method and system |
| CN117474297A (en) * | 2023-12-27 | 2024-01-30 | 南京信息工程大学 | Optimization method for ship berth and quay crane distribution for automatic wharf |
| CN117474297B (en) * | 2023-12-27 | 2024-04-16 | 南京信息工程大学 | Optimization method for ship berth and quay crane distribution for automatic wharf |
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