Bulk cargo terminal storage yard stack position distribution method and system based on simulation optimization

Abstract

The invention provides a bulk cargo wharf storage yard stacking position distribution method and system based on simulation optimization. The method comprises the steps that S1, train information of a port wharf and a storage yard stacking state are acquired; s2, dividing available stacking areas of different bulk cargoes, and based on an improved whale optimization algorithm, carrying out preliminary selection on stockable stacking positions to obtain a preliminary stacking position distribution result; s3, adjusting the preliminary stack position distribution result based on a fixed-random combined stockpiling strategy under operation balance, and determining a final stockpiling stack position; and S4, obtaining a simulation layout and simulation operation logic, constructing a simulation model, inputting wharf parameters, and using the simulation model to operate a stack position allocation method to obtain a bulk cargo stockpiling scheme. Aiming at the problems of insufficient storage yard space utilization, unscientific stack position distribution and the like in the bulk cargo wharf, the invention provides a reasonable and efficient storage yard stack position distribution method, so that the continuity of storage yard operation tasks is ensured, and the stockpiling capacity of the storage yard of the bulk cargo wharf is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ship operation, and more specifically, to a bulk cargo terminal yard stack allocation method and system based on simulation optimization. Background Art

[0002] Under the background of global economic integration and rapid development of information network, water transport has gradually become one of the important ways of transporting bulk goods in the world. Bulk cargo occupies a very important position in water transport and accounts for a large proportion of terminal business. As an important part of the terminal, the yard can effectively improve the storage capacity of the yard and the overall operation capacity of the terminal by reasonably allocating its space resources. At the same time, when storing goods, it is necessary to consider a variety of factors, such as the type of bulk cargo, the weight of bulk cargo, the occupancy of the operation line, and the storage status of the yard, which will affect the allocation results of the stacking position. There are also various problems in the current yard scheduling methods of terminal enterprises, such as: the yard bulk cargo often adopts extensive storage methods, and the space utilization is not sufficient; the local plan is reasonable, but lacks overall overall arrangement, which seriously affects the operation capacity of bulk cargo terminals. Therefore, exploring the allocation method of the stacking position of the bulk cargo terminal yard is of great significance to improve the operation efficiency of the yard and enhance the competitiveness of the terminal. Summary of the invention

[0003] In view of the technical problems existing in the prior art, the present invention provides a method and system for allocating stack positions in a bulk cargo terminal yard based on simulation optimization. The purpose of the present invention is to avoid the problems existing in the existing yards and improve the yard storage capacity of the bulk cargo terminal. Factors such as the yard storage status, bulk cargo types, occupancy of operation lines, and the impact of unloading operations on the stack position allocation results are taken into account, thereby improving the yard storage capacity and ensuring the smooth progress of storage operations, which is conducive to improving the operating capacity of the bulk cargo terminal.

[0004] According to a first aspect of the present invention, a method for allocating stacking spaces in a bulk cargo terminal yard based on simulation optimization is provided, comprising the following steps:

[0005] Step S1: Obtaining the train information and yard storage status information of the port terminal;

[0006] Step S2: Divide the available stacking areas for different bulk cargoes, and make a preliminary selection of available stacking locations based on the improved whale optimization algorithm to obtain a preliminary stacking location allocation result;

[0007] Step S3: adjusting the preliminary stacking position allocation result based on the fixed-random combined stacking strategy under job balance to determine the final stacking position;

[0008] Step S4: obtain the simulation layout and simulation operation logic, build a simulation model, input terminal parameters, use the simulation model to run the stack allocation method, and obtain the bulk cargo storage plan.

[0009] Based on the above technical solution, the present invention can also make the following improvements.

[0010] Optionally, the train information includes: train arrival time, types of bulk cargo carried by the train, weight of bulk cargo carried by the train, queue status of the train in the waiting area and train model;

[0011] The stockpile status of the yard includes: the types of bulk cargo in the yard, the occupancy of the yard operation lines, the specific stacking locations of the stacked bulk cargo and the locations of the vacant stacking locations.

[0012] Optionally, dividing available storage areas for different bulk cargoes includes:

[0013] Obtain the types of bulk cargo at the port in previous years, the turnover of each bulk cargo, and the total turnover of bulk cargo throughout the year in previous years, and calculate the proportion of the turnover of each bulk cargo in the total turnover;

[0014] Sort the bulk cargo turnover ratios by size, and divide the bulk cargoes into high-turnover bulk cargoes and low-turnover bulk cargoes according to a certain ratio, to obtain high-turnover bulk cargo category information and low-turnover bulk cargo category information;

[0015] The yard is gridded, subdivided into small grids with fixed volumes, and the number of grids in each yard is determined according to the length of each yard;

[0016] In the grid-based yard, for cargoes with large turnover, the yard is divided proportionally in the area close to the front of the wharf; for cargoes with small turnover, the yard is divided proportionally in the area far away from the front of the wharf.

[0017] Optionally, the preliminary selection of the stackable stacking positions based on the improved whale optimization algorithm to obtain the preliminary stacking position allocation result includes:

[0018] Initialize parameters, determine individual dimension D, search space range [lb, ub], determine number of whales N and maximum number of iterations;

[0019] Determine the encoding and decoding methods of individual whales, initialize the population construction according to the types of cargo carried by the train and the order of train arrival, and calculate the individual fitness values ​​of the whale population with the goal of maximizing the utilization of the yard space;

[0020] Calculate the fitness value of each whale according to the fitness function and find the most well-fed whale;

[0021] Update the whale's position, introduce adaptive inertia weight to improve the algorithm, and retain the current global optimal solution;

[0022] Determine whether the algorithm has reached the maximum number of iterations. If so, the algorithm terminates and obtains the final stacking information; otherwise, go to the step of calculating the fitness value of each whale.

[0023] Optionally, the method of introducing an adaptive inertia weight to improve the algorithm and retaining the current global optimal solution includes: adaptively changing the inertia weight according to the number of iterations, and the formula is as follows:

[0024]

[0025] In the above formula, f is a random number between [0,1], which makes the inertia weight randomly distributed, t is the number of iterations, and T is the total number of iterations. After introducing the inertia weight, the position update formula is:

[0026] X(t+1)=ω(t)X pre -A×D 1

[0027] X(t+1)=ω(t)X rand -A×D 2

[0028] In the above formula, X pre represents the current optimal whale position, X rand is the position of a random whale, X(t) represents the current position of the whale, t is the current iteration number, A is the coefficient, and D 1 Indicates the distance between the current whale and the optimal whale; D 2 Indicates the distance between the current whale and the random whale.

[0029] Optionally, the stacking strategy based on the fixed-random combination under job balance adjusts the preliminary stacking position allocation result to determine the final stacking position, including:

[0030] For bulk cargo types, determine whether they are high turnover cargo or low turnover cargo:

[0031] If it is a high-turnover cargo, determine the yard with low occupancy rate; if the initial storage location obtained by the whale optimization algorithm is in the yard, the initial storage location is the final storage location; if not, traverse the storage area of ​​bulk cargo in the yard to obtain all available vacant locations for storage;

[0032] If it is a low-turnover cargo, traverse the storage area divided by the bulk cargo to find out whether the bulk cargo exists in the storage area. If the bulk cargo exists, traverse the adjacent positions of the stacking position of the bulk cargo to determine whether there are available vacant stacking positions. If so, calculate the occupancy rates of the stacking yards where the available vacant stacking positions are located; sort the calculated occupancy rates to find the stacking yard with the lowest occupancy rate. If the initial stacking stacking position is in the stacking yard, the position is the stacking stacking position; otherwise, the vacant stacking position adjacent to the stacking position where the bulk cargo is stored is the stacking stacking position of the bulk cargo;

[0033] If not, all available storage areas are traversed to find all available stacking locations, and storage space is randomly selected from all available stacking locations, which is the bulk cargo storage stacking location.

[0034] Optionally, among all the available free stacking positions, if there are available free stacking positions, the available space set of the free stacking positions is set to (S1, S2, ..., Sn), and the available space set (S1, S2, ..., Sn) is sorted to obtain the stacking position with the smallest available space; the stacking position with the smallest available space is the stacking position, and among the stacking positions, the area close to the front edge of the wharf and close to the stacked positions is the storage location of the bulk cargo;

[0035] If there is no available stacking position, the initial stacking stacking position will be the final stacking location.

[0036] Optionally, the simulation layout includes: obtaining overall layout information of the studied terminal based on the terminal, the overall layout information including yard size, number of yards, operation line layout, and position of car dumpers; the terminal parameters include: train arrival time, train arrival time interval, train load, belt conveyor speed, yard length, yard width, number of yards, yard stockpile, stacker-reclaimer operating efficiency, number of stackers, car dumper operating efficiency and number of car dumpers.

[0037] Optionally, the simulation operation logic includes unloading operation logic and yard operation logic; the unloading operation logic includes:

[0038] When a train arrives, obtain the type and weight of bulk cargo carried by the train;

[0039] Determine whether the car tipper is idle. If it is idle, execute the yard operation logic. Otherwise, wait in the shunting area. The train enters the car tipper to perform the car tipping operation and executes the yard operation logic.

[0040] According to a second aspect of the present invention, there is provided a bulk cargo terminal yard stack allocation system based on simulation optimization, comprising:

[0041] Data acquisition module, used to obtain the train information and yard storage status information of the port terminal;

[0042] The module for obtaining the preliminary stacking position allocation result is used to divide the available stacking areas for different bulk cargoes, make a preliminary selection of the available stacking positions based on the improved whale optimization algorithm, and obtain the preliminary stacking position allocation result;

[0043] The final stacking position acquisition module is used to adjust the preliminary stacking position allocation result based on the fixed-random combination stacking strategy under job balance to determine the final stacking position;

[0044] The bulk cargo storage plan acquisition module is used to obtain the simulation layout and simulation operation logic, build a simulation model, input terminal parameters, use the simulation model to run the stack allocation method, and obtain the bulk cargo storage plan.

[0045] Technical effects and advantages of the present invention:

[0046] The present invention proposes a method and system for allocating stack positions in a bulk cargo terminal yard based on simulation optimization. The improved whale optimization algorithm is first used to obtain a preliminary stack position allocation result, and then the fixed-random combined stockpiling strategy under the proposed operation balance is used to adjust the preliminary stack position allocation result. The effectiveness of the scheme is verified by simulation deduction. This scheme takes into account multiple factors such as the yard stockpiling status, bulk cargo types, occupancy of the operation line, and the impact of unloading operations on the stack position allocation results. It can improve the yard's stockpiling capacity, while also ensuring the smooth progress of the stockpiling operation, which helps to improve the operating capacity of the bulk cargo terminal.

[0047] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of the steps of a bulk cargo terminal yard stack allocation method based on simulation optimization provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of the storage yard division provided by an embodiment of the present invention;

[0050] Figure 3 A flow chart of obtaining initial stockpile positions using a whale optimization algorithm provided in an embodiment of the present invention;

[0051] Figure 4 A flow chart of a fixed-random combined stockpiling strategy under job balance provided by an embodiment of the present invention;

[0052] Figure 5 A flow chart of unloading operations provided by an embodiment of the present invention;

[0053] Figure 6 A flow chart of yard operations provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] It can be understood that, based on the defects in the background technology, the embodiment of the present invention proposes a bulk cargo terminal yard stack allocation method based on simulation optimization, specifically as follows: Figure 1 As shown, the allocation method comprises the following steps:

[0056] Step S1: Obtaining the train information and yard storage status information of the port terminal;

[0057] In the embodiment of the present invention, the train information includes: train arrival time, type of bulk cargo carried by the train, weight of bulk cargo carried by the train, and queue status of the train in the waiting area;

[0058] The storage status of the yard includes: the types of bulk cargo in the yard, the occupancy of the yard operation lines, the specific stacking locations of the stacked bulk cargo, and the locations of the vacant stacks.

[0059] Step S2: Divide the available stacking areas for different bulk cargoes, and make a preliminary selection of available stacking locations based on the improved whale optimization algorithm to obtain a preliminary stacking location allocation result;

[0060] Furthermore, the division of available storage areas for different bulk cargoes includes:

[0061] Step S211: Obtain the types of bulk cargo at the port in previous years, the turnover of each bulk cargo, and the total turnover of bulk cargo throughout the year in previous years, and calculate the proportion of the turnover of each bulk cargo to the total turnover; the proportion calculation formula is as follows:

[0062]

[0063] Among them, Ri is the proportion of the i-th type of bulk cargo, ti is the turnover of the i-th type of bulk cargo, and T is the total turnover;

[0064] Step S212: sorting the bulk cargo turnover ratios (R1, R2, ..., Rn) by size, and dividing the bulk cargo into high-turnover bulk cargo and low-turnover bulk cargo according to a certain ratio, to obtain high-turnover bulk cargo category information and low-turnover bulk cargo category information;

[0065] Step S213: gridding the stockyard into small grids with fixed volumes. The width of the grids, d, is generally 3 to 5 m. The number of grids in each stockyard is determined according to the length of each stockyard. For example, if the length of the stockyard is L, the number of grids, n, is: n=L / d.

[0066] Step S214: For cargoes with large turnover and cargoes with small turnover, in the grid yard, for cargoes with large turnover, the yard is divided proportionally in the area close to the front of the wharf; for cargoes with small turnover, the yard is divided proportionally in the area far from the front of the wharf. The division results are as follows: Figure 2 shown.

[0067] References Figure 3 As shown, the method of performing preliminary selection of stackable stacking locations based on the improved whale optimization algorithm to obtain preliminary stacking location allocation results includes the following steps:

[0068] Step S221: Initialize parameters, determine individual dimension D, search space range [lb, ub], determine number of whales N and maximum number of iterations T;

[0069] Step S222: Determine the coding and decoding methods of individual whales, initialize the population construction according to the types of goods carried by the train and the order of train arrival, and calculate the individual fitness values ​​of the whale population with the goal of maximizing the utilization of the yard space;

[0070] Step S223: Calculate the fitness value of each whale according to the fitness function, and find the most well-fed whale X pre ;

[0071] Step S224: Update the whale position, introduce adaptive inertia weight to improve the algorithm, and retain the current global optimal solution;

[0072] Step S225: Determine whether the algorithm has reached the maximum number of iterations T. If so, the algorithm terminates and obtains the final stack position information; otherwise, go to step S223.

[0073] In step S224, the specific method of updating the whale position is as follows:

[0074] A=2×a×rand-a

[0075] In the above formula, A is a coefficient used to control the range of the whale's movement. a gradually decreases from 2 to 0 during the iteration process, and rand is a random number between 0 and 1. When |A|>1, the whale will conduct a wider exploration in the search space, which helps to avoid local optimal solutions. When |A|<1, the algorithm focuses more on refining the current solution, that is, intensive search around the current optimal solution. As a gradually decreases from 2 to 0, the algorithm gradually transitions from global search to local search.

[0076] When rand < 0.5 and |A ≤ 1, it is the stage of surrounding the prey, and the position update formula is:

[0077] X(t+1)=X pre -A×D 1

[0078] D 1 =|2×rand×X pre -X(t)|

[0079] In the above formula, D1 is the distance between the current whale and the optimal whale, X pre is the position of the current optimal solution, t is the current iteration number, and X is the position of the current solution;

[0080] When rand>0.5, the spiral updates its position;

[0081] X(t+1)=D pre ×e bl ×cos(2πl)+X pre

[0082] D pre =|X pre -X(t)|

[0083] In the above formula, D pre represents the distance between the i-th individual and the optimal individual, b is a constant, and l is a random number between [-1,1];

[0084] When rand < 0.5, |A| > 1, it is the prey search phase, and the position update formula is:

[0085] X(t+1)=X rand -A×D 2

[0086] D 2 =|2×rand×X rand -X(t)|

[0087] In the above formula, Xrand is the position of a random whale;

[0088] In step S224, the algorithm is improved by introducing the adaptive inertia weight, and the specific method is as follows:

[0089] The inertia weight is adaptively changed according to the number of iterations. The formula is as follows:

[0090]

[0091] In the above formula, f is a random number between [0,1], which makes the inertia weight randomly distributed, t is the number of iterations, and T is the total number of iterations.

[0092] After introducing the inertia weight, the position update formula is:

[0093] X(t+1)=ω(t)X pre -A×D 1

[0094] X(t+1)=ω(t)X rand -A×D 2

[0095] In the above formula, X pre represents the current optimal whale position, X rand is the position of a random whale, X(t) represents the current position of the whale, t is the current iteration number, A is the coefficient, and D 1 Indicates the distance between the current whale and the optimal whale; D 2 Indicates the distance between the current whale and the random whale.

[0096] Step S3: adjusting the preliminary stacking position allocation result based on the fixed-random combined stacking strategy under job balance to determine the final stacking position;

[0097] References Figure 4 As shown, the fixed-random combination stockpiling strategy based on job balance includes:

[0098] Step S31: for the bulk cargo type, determine whether it is a high-turnover cargo type or a low-turnover cargo type. If it is a high-turnover cargo type, execute the next step; if it is a low-turnover cargo type, execute S35;

[0099] Step S32: For goods with large turnover, assuming that the yard numbers where the goods are divided into stacking areas are i and j respectively, traverse the yards i and j respectively to find all the stored spaces in each yard. For example, the set of all the stored spaces in the yard i is (Wi1, Wi2, ...Win), and the set of all the stored spaces in the yard j is (Wj1, Wj2, ...Wjn). For each yard, sum Wi1+Wi2+ ...+Win and Wj1+Wj2+ ...+Wjn respectively, compare the two values, and find the yard with the smaller sum. This yard has a lower yard occupancy rate.

[0100] Step S33: Determine a yard with low occupancy rate. If the initial stacking position obtained by the whale optimization algorithm is in the yard, the initial stacking position is the final stacking position; if not, traverse the storage area of ​​the bulk cargo in the yard to obtain all available free stacking positions. If there are available free stacking positions, set the available space set of the free stacking positions to (S1, S2, ..., Sn), and perform the next operation; if there are no available stacking positions, the initial stacking position is the final stacking position;

[0101] Step S34: sorting the available space set (S1, S2, ..., Sn) to obtain the stacking position with the smallest available space; the stacking position with the smallest available space is the stacking position, and among the stacking positions, the area close to the front of the wharf and close to the stacked positions is the storage location of the bulk cargo;

[0102] Step S35: for the type of bulk cargo with small turnover, traverse the storage area divided by the bulk cargo to find out whether the bulk cargo exists in the storage area; if the bulk cargo exists, execute the next operation; if not, execute step S38;

[0103] Step S36: Traverse the adjacent positions of the bulk cargo stacks to determine whether there are available vacant stacks. If so, calculate the occupancy rates of the stacks where the available vacant stacks are located. If not, execute step S38.

[0104] Step S37: sorting the calculated occupancy rates to find the stacking yard with the lowest occupancy rate. If the initial stacking position is in the stacking yard, the position is the stacking position; otherwise, the vacant stacking position adjacent to the stacking position where the bulk cargo is stored is the stacking position for the bulk cargo.

[0105] Step S38: Traverse all available stacking areas to find all available stacking positions, and randomly select a stacking space from all available stacking positions, which is the bulk cargo storage stacking position.

[0106] Step S4: obtaining the simulation layout and simulation operation logic, building a simulation model, running the stacking position allocation method, and obtaining a specific yard stockpiling plan;

[0107] The simulation layout includes: obtaining the overall layout information of the studied terminal, including yard size, number of yards, operation line layout, and dumper position; the simulation operation logic includes unloading operation logic and yard operation logic. The terminal parameters include: train arrival time, train arrival time interval, train load, belt conveyor speed, yard length, yard width, number of yards, yard stockpile, stacker-reclaimer operation efficiency, number of stackers, dumper operation efficiency, and number of dumpers.

[0108] The unloading operation logic is as follows: Figure 5As shown, the following steps are included:

[0109] Step S411: a train arrives, and the type and load of bulk cargo carried by the train are obtained;

[0110] Step S412: determine whether the tipping machine is idle, if it is idle, execute the next step, otherwise, wait in the shunting area;

[0111] Step S413: The train enters the car tipping machine to perform the tipping operation and execute the next step.

[0112] Yard operation logic Figure 6 As shown, the following steps are included:

[0113] Step S421: After the train enters the dumper, the stockpiling position is obtained by using the whale optimization algorithm and the fixed-random combined stockpiling strategy under job balance;

[0114] Step S422: obtaining a stacking location that can be stored, and obtaining the stacking yard serial number where the stacking location is located;

[0115] Step S423: determine whether the stacker-reclaimer corresponding to the stockpile is idle, if so, execute the next operation, otherwise, wait;

[0116] Step S424: Select a belt conveyor route to transport the bulk cargo to the stacker-reclaimer;

[0117] Step S425: the stacker-reclaimer performs stacking operation.

[0118] According to a second aspect of the present invention, there is provided a bulk cargo terminal yard stack allocation system based on simulation optimization, comprising:

[0119] Data acquisition module, used to obtain the train information and yard storage status information of the port terminal;

[0120] The module for obtaining the preliminary stacking position allocation result is used to divide the available stacking areas for different bulk cargoes, make a preliminary selection of the available stacking positions based on the improved whale optimization algorithm, and obtain the preliminary stacking position allocation result;

[0121] The final stacking position acquisition module is used to adjust the preliminary stacking position allocation result based on the fixed-random combination stacking strategy under job balance to determine the final stacking position;

[0122] The bulk cargo storage plan acquisition module is used to obtain the simulation layout and simulation operation logic, build a simulation model, input terminal parameters, use the simulation model to run the stack allocation method, and obtain the bulk cargo storage plan.

[0123] It can be understood that the bulk cargo terminal yard stack position allocation system based on simulation optimization provided by the present invention corresponds to the bulk cargo terminal yard stack position allocation method based on simulation optimization provided by the aforementioned embodiments. The relevant technical features of the bulk cargo terminal yard stack position allocation system based on simulation optimization can refer to the relevant technical features of the bulk cargo terminal yard stack position allocation method based on simulation optimization, which will not be repeated here.

[0124] In summary, the embodiment of the present invention proposes a method and system for allocating stack positions in a bulk cargo terminal yard based on simulation optimization. This scheme takes into account various factors such as the yard storage status, bulk cargo types, occupancy of operation lines, and the impact of unloading operations on the stack position allocation results. It can improve the yard's storage capacity while ensuring the smooth progress of storage operations, which helps to improve the operating capacity of the bulk cargo terminal.

[0125] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0126] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for allocating stacking positions in bulk cargo terminal yards based on simulation optimization, characterized in that: The following steps are involved: Step S1: Obtaining the train information and yard storage status information of the port terminal; Step S2: Divide the available stacking areas for different bulk cargoes, and make a preliminary selection of available stacking locations based on the improved whale optimization algorithm to obtain a preliminary stacking location allocation result; Step S3: adjusting the preliminary stacking position allocation result based on the fixed-random combined stacking strategy under job balance to determine the final stacking position; Step S4: obtain the simulation layout and simulation operation logic, build a simulation model, input terminal parameters, use the simulation model to run the stack allocation method, and obtain the bulk cargo storage plan.

2. The method for allocating stacking spaces in bulk cargo terminal yards based on simulation optimization according to claim 1, characterized in that: The train information includes: train arrival time, type of bulk cargo carried by the train, weight of bulk cargo carried by the train, train queue situation in the waiting area and train model; The stockpile status of the yard includes: the types of bulk cargo in the yard, the occupancy of the yard operation lines, the specific stacking locations of the stacked bulk cargo and the locations of the vacant stacking locations.

3. The method for allocating stacking spaces in bulk cargo terminal yards based on simulation optimization according to claim 1, characterized in that: The division of available storage areas for different bulk cargoes includes: Obtain the types of bulk cargo at the port in previous years, the turnover of each bulk cargo, and the total turnover of bulk cargo throughout the year in previous years, and calculate the proportion of the turnover of each bulk cargo in the total turnover; Sort the bulk cargo turnover ratios by size, and divide the bulk cargoes into high-turnover bulk cargoes and low-turnover bulk cargoes according to a certain ratio, to obtain high-turnover bulk cargo category information and low-turnover bulk cargo category information; The yard is gridded, subdivided into small grids with fixed volumes, and the number of grids in each yard is determined according to the length of each yard; In the grid-based yard, for cargoes with large turnover, the yard is divided proportionally in the area close to the front of the wharf; for cargoes with small turnover, the yard is divided proportionally in the area far away from the front of the wharf.

4. The method for allocating stacking spaces in bulk cargo terminal yards based on simulation optimization according to claim 1, characterized in that: The improved whale optimization algorithm is used to preliminarily select the stacking positions that can be stored, and the preliminary stacking position allocation results obtained include: Initialize parameters, determine individual dimension D, search space range [lb, ub], determine number of whales N and maximum number of iterations T; Determine the encoding and decoding methods of individual whales, initialize the population construction according to the types of cargo carried by the train and the order of train arrival, and calculate the individual fitness values ​​of the whale population with the goal of maximizing the utilization of the yard space; Calculate the fitness value of each whale according to the fitness function and find the most well-fed whale; Update the whale's position, introduce adaptive inertia weight to improve the algorithm, and retain the current global optimal solution; Determine whether the algorithm has reached the maximum number of iterations T. If so, the algorithm terminates and obtains the final stack information; otherwise, go to the step of calculating the fitness value of each whale.

5. The method for allocating stacking space in bulk cargo terminal yard based on simulation optimization according to claim 4, characterized in that: The method of introducing the adaptive inertia weight to improve the algorithm and retain the current global optimal solution includes: adaptively changing the inertia weight according to the number of iterations, and the formula is as follows: In the above formula, f is a random number between [0,1], which makes the inertia weight randomly distributed, t is the number of iterations, and T is the total number of iterations. After introducing the inertia weight, the position update formula is: X(t+1)=ω(t)X pre -A×D1 X(t+1)=ω(t)X rand -A×D2 In the above formula, X pre represents the current optimal whale position, X rand is the position of a random whale, X(t) represents the current position of the whale, t is the current iteration number, A is the coefficient, D1 represents the distance between the current whale and the optimal whale; D2 represents the distance between the current whale and the random whale.

6. The method for allocating stacking spaces in bulk cargo terminal yards based on simulation optimization according to claim 1, characterized in that: The stacking strategy based on the fixed-random combination under the operation balance adjusts the preliminary stacking position allocation result to determine the final stacking position, including: For bulk cargo types, determine whether they are high-turnover cargo or low-turnover cargo; If it is a high-turnover cargo, determine the yard with low occupancy rate; if the initial storage location obtained by the whale optimization algorithm is in the yard, the initial storage location is the final storage location; if not, traverse the storage area of ​​bulk cargo in the yard to obtain all available vacant locations for storage; If it is a low-turnover cargo, traverse the stackable stacking area divided by the bulk cargo to find out whether the bulk cargo exists in the stackable stacking area. If the bulk cargo exists, traverse the adjacent positions of the stacking position of the bulk cargo to determine whether there are available idle stacking positions. If so, calculate the occupancy rates of the stacking yards where the available idle stacking positions are located, sort the calculated occupancy rates, and find the stacking yard with the lowest occupancy rate. If the initial stacking stacking position is in the stacking yard, the position is the stacking stacking position; otherwise, the idle stacking position adjacent to the stacking position where the bulk cargo is stored is the stacking stacking position for the bulk cargo; if the bulk cargo does not exist, traverse all stackable stacking areas to find all available stacking positions, and randomly select storage space from all available stacking positions, which is the bulk cargo storage stacking position.

7. The method for allocating stacking spaces in bulk cargo terminal yards based on simulation optimization according to claim 6, characterized in that: Among all the available free stacking positions, if there are available free stacking positions, set the available space set of the free stacking positions as (S1, S2, ..., Sn), sort the available space set (S1, S2, ..., Sn) to obtain the stacking position with the smallest available space; the stacking position with the smallest available space is the stacking position, and among the stacking positions, the area close to the front edge of the wharf and close to the stacked positions is the storage location of the bulk cargo; If there is no available stacking position, the initial stacking stacking position will be the final stacking location.

8. The method for allocating stacking spaces in bulk cargo terminal yards based on simulation optimization according to claim 1, characterized in that: The simulation layout includes: obtaining the overall layout information of the studied terminal according to the overall layout information including the yard size, the number of yards, the operation line layout and the position of the dumper; the terminal parameters include: the train arrival time, the train arrival time interval, the train load, the belt speed of the conveyor belt, the yard length, the yard width, the number of yards, the yard stockpile, the stacker-reclaimer operating efficiency, the number of stackers, the dumper operating efficiency and the number of dumpers.

9. The method for allocating stacking space in bulk cargo terminal yard based on simulation optimization according to claim 1, characterized in that: The simulation operation logic includes unloading operation logic and yard operation logic; the unloading operation logic includes: When a train arrives, obtain the type and weight of bulk cargo carried by the train; Determine whether the car tipper is idle. If it is idle, execute the yard operation logic. Otherwise, wait in the shunting area. The train enters the car tipper to perform the car tipping operation and executes the yard operation logic.

10. A bulk cargo terminal yard stack allocation system based on simulation optimization, characterized in that: include: Data acquisition module, used to obtain the train information and yard storage status information of the port terminal; The module for obtaining the preliminary stacking position allocation result is used to divide the available stacking areas for different bulk cargoes, make a preliminary selection of the available stacking positions based on the improved whale optimization algorithm, and obtain the preliminary stacking position allocation result; The final stacking position acquisition module is used to adjust the preliminary stacking position allocation result based on the fixed-random combination stacking strategy under job balance to determine the final stacking position; The bulk cargo storage plan acquisition module is used to obtain the simulation layout and simulation operation logic, build a simulation model, input terminal parameters, use the simulation model to run the stack allocation method, and obtain the bulk cargo storage plan.

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