A simulation optimization-based bulk cargo terminal yard stacking position allocation method and system

Abstract

The application provides a bulk cargo terminal yard stacking position allocation method and system based on simulation optimization, and the method comprises the following steps: S1, obtaining train information and yard stacking state of a port terminal; S2, dividing different bulk cargo available stacking areas, preliminarily selecting stackable stacking positions based on an improved whale optimization algorithm, and obtaining a preliminary stacking position allocation result; S3, adjusting the preliminary stacking position allocation result based on a fixed-random combined stacking strategy under operation balance to determine the final stacking position; S4, obtaining a simulation layout and simulation operation logic, constructing a simulation model, inputting terminal parameters, running the stacking position allocation method by using the simulation model, and obtaining a bulk cargo stacking scheme. The application proposes a reasonable and efficient yard stacking position allocation method aiming at the problems of insufficient utilization of yard space, unscientific stacking position allocation and the like in a bulk cargo terminal, guarantees the continuity of yard operation tasks, and improves the stacking capacity of the bulk cargo terminal yard.

Description

Technical Field

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

[0002] Against the backdrop of global economic integration and the rapid development of information networks, waterway transportation has gradually become a key mode of transporting bulk cargo worldwide. Bulk cargo plays a crucial role in waterway transportation and accounts for a significant portion of terminal operations. As a crucial component of a terminal, the storage yard, through the rational allocation of its spatial resources, can effectively improve its storage capacity and enhance the terminal's overall operational capabilities. Furthermore, various factors must be considered when stockpiling cargo, such as the type and weight of bulk cargo, the occupancy of work lines, and the storage status of the yard, all of which influence the allocation of stacking space. Current yard scheduling methods employed by terminal operators also present various issues, such as the widespread use of extensive storage methods for bulk cargo, resulting in inadequate space utilization, and the lack of overall coordination and planning, which severely impact the operational capacity of bulk cargo terminals. Therefore, exploring methods for allocating stacking space in bulk cargo terminal yards is crucial for improving yard operational efficiency and enhancing terminal competitiveness. Summary of the Invention

[0003] In response to 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 yard and improve the yard storage capacity of the bulk cargo terminal. The method takes into account factors such as the yard storage status, the type of bulk cargo, the occupancy of the operation line, and the impact of the unloading operation on the stack position allocation result, thereby improving the yard storage capacity and ensuring the smooth progress of the storage operation, which helps to improve 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 location allocation result based on the fixed-random combined stacking strategy under job balance to determine the final stacking location;

[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] On the basis of the above technical solution, the present invention can also make the following improvements.

[0010] Optionally, 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 status in the waiting area, and train model;

[0011] The storage yard status includes: the type of bulk cargo in the yard, the occupancy of the yard operation line, the specific stacking location of the stacked bulk cargo and the location of the vacant stacking space.

[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 in previous years, and calculate the proportion of each bulk cargo turnover in the total turnover;

[0014] Sort the bulk cargo turnover ratio by size, and divide the bulk cargo into high-turnover bulk cargo and low-turnover bulk cargo according to a certain ratio, so as to obtain the type information of high-turnover bulk cargo and low-turnover bulk cargo;

[0015] Grid the storage yard, subdivide it into small grids with fixed volumes, and determine the number of grids in each yard based on 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 terminal; for cargoes with small turnover, the yard is divided proportionally in the area far away from the front of the terminal.

[0017] Optionally, the preliminary selection of available stacking locations based on the improved whale optimization algorithm to obtain a preliminary stacking location allocation result includes:

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

[0019] Determine the encoding and decoding methods for individual whales, initialize the population based on the cargo types and arrival order of the trains, and calculate the individual fitness values ​​of the whale population with the goal of maximizing yard space utilization;

[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 and introduce adaptive inertia weights 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, as shown in the following formula:

[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×D1

[0027] X(t+1)=ω(t)X rand -A×D2

[0028] In the above formula, X pre Indicates 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.

[0029] Optionally, the adjusting of the preliminary stacking location allocation result based on the fixed-random combined stacking strategy under job balance to determine the final stacking location includes:

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

[0031] If the cargo is a high-turnover cargo, a yard with low occupancy is identified. If the initial storage location determined by the Whale Optimization Algorithm is in the yard, the initial storage location becomes the final storage location. If not, the bulk cargo storage area within the yard is traversed to obtain all available vacant storage locations.

[0032] If the cargo is of low turnover volume, the stacking area divided by the bulk cargo is traversed to find out whether the bulk cargo exists in the stacking area. If the bulk cargo exists, the adjacent positions of the stacking location of the bulk cargo are traversed to determine whether there are available vacant stacking locations. If so, the occupancy rates of the stacking yards where the available vacant stacking locations are located are calculated respectively. The calculated occupancy rates are sorted to find the stacking yard with the lowest occupancy rate. If the initial stacking location is in the stacking yard, the location is used as the stacking location. Otherwise, the vacant stacking location adjacent to the stacking location where the bulk cargo is already stored is used as the stacking location for 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, if there is an available idle stack position among all the obtained idle stack positions, set the available space set of the idle stack positions as (S1, S2, ..., Sn), sort the available space set (S1, S2, ..., Sn) by size, and obtain the stack position with the smallest available space; the stack position with the smallest available space is the stacking stack position, and among the stackable stack positions, the area close to the front edge of the wharf and close to the stacked stack positions is the storage location of the bulk cargo;

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

[0036] Optionally, the simulation layout includes: obtaining overall layout information based on the studied terminal, the overall layout information including yard size, number of yards, operation line layout, and tipper position; 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 volume, stacker-reclaimer operating efficiency, number of stackers, tipper operating efficiency, and number of tippers.

[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 for tipping and executes the yard operation logic.

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

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

[0042] The module for obtaining preliminary stacking location allocation results is used to divide the available stacking areas for different bulk cargoes, make preliminary selections of available stacking locations based on the improved whale optimization algorithm, and obtain preliminary stacking location allocation results.

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

[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] This paper proposes a method and system for allocating stack slots in bulk cargo terminal yards based on simulation optimization. This method first uses an improved whale optimization algorithm to derive preliminary stack slot allocation results, then adjusts these preliminary allocations using a proposed fixed-random combined stacking strategy under operational balance. The effectiveness of this scheme is verified through simulation. This scheme considers multiple factors, such as the yard's stockpiling status, bulk cargo type, and line occupancy, as well as the impact of unloading operations on the stack slot allocation results. This method can improve the yard's stockpiling capacity while ensuring smooth stockpiling operations, thereby enhancing the operational capacity of bulk cargo terminals.

[0047] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be 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 a method for allocating stacking spaces in a bulk cargo terminal yard based on simulation optimization is provided in an embodiment of the present invention;

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

[0050] Figure 3 A flowchart for obtaining initial stacking 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 balancing provided by an embodiment of the present invention;

[0052] Figure 5 A flowchart of the unloading operation provided by an embodiment of the present invention;

[0053] Figure 6 This is a flow chart of yard operations provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0055] It is understandable 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 includes the following steps:

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

[0057] In an 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 train queue status in the waiting area;

[0058] The storage status of the yard includes: the type of bulk cargo in the yard, the occupancy of the yard operation line, the specific stacking location of the stacked bulk cargo, and the location of the vacant stack spaces.

[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 type of bulk cargo, and the total turnover of bulk cargo in previous years, and calculate the proportion of the turnover of each type of 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, thereby obtaining high-turnover bulk cargo category information and low-turnover bulk cargo category information;

[0065] Step S213: Gridding the storage yard into small grids with fixed volumes. The width of each grid, d, is generally 3 to 5 meters. The number of grids in each storage yard is determined based on the length of the storage yard. For example, if the length of the storage yard is L, the number of grids, n, is: n = L / d.

[0066] Step S214: For cargoes with high turnover and cargoes with low turnover, in the grid yard, for cargoes with high turnover, the yard is divided proportionally in the area close to the front of the wharf; for cargoes with low 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 improved whale optimization algorithm is used to preliminarily select the stackable stacking locations and obtain the preliminary stacking location allocation result, which includes the following steps:

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

[0069] Step S222: Determine the coding and decoding methods for individual whales, construct an initial population based on the types of cargo carried by the trains and the order in which the trains arrive at the port, and calculate the fitness values ​​of the individual whale populations 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's position, introduce adaptive inertia weights 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 that controls the whale's range of 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 explores more extensively in the search space, helping to avoid local optima. When |A| < 1, the algorithm focuses more on refining the current solution, performing an intensive search around the current optimal solution. As a 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×D1

[0078] D1=|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 number of iterations, 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 and |A| > 1, it is the prey search phase, and the position update formula is:

[0085] X(t+1)=X rand -A×D2

[0086] D2=|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. 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×D1

[0094] X(t+1)=ω(t)X rand -A×D2

[0095] In the above formula, X pre Indicates 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.

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

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

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

[0099] Step S32: For goods with large turnover, assuming that the yards where the storage areas for this goods are divided are numbered 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 stored spaces in yard i is (Wi1, Wi2, ...Win), and the set of all stored spaces in yard j is (Wj1, Wj2, ...Wjn). For each yard, sum all the stored spaces 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 occupancy rate.

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

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

[0102] Step S35: For the type of bulk cargo with a small turnover volume, 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 step; if not, execute step S38;

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

[0104] Step S37: Sort the calculated occupancy rates and find the stacking yard with the lowest occupancy rate. If the initial stacking location is in the stacking yard, then the location is used as the stacking location. Otherwise, the vacant stacking location adjacent to the stacking location where the bulk cargo is already stored is used as the stacking location for the bulk cargo.

[0105] Step S38: Traverse all available storage areas to find all available stacking locations, and randomly select storage space from all available stacking locations, which are bulk cargo storage locations.

[0106] Step S4: Acquire the simulation layout and simulation operation logic, build a simulation model, run the stack allocation method, and obtain a specific yard storage plan;

[0107] The simulated layout includes obtaining overall layout information for the terminal under study, including yard size, number of yards, operation line layout, and dumper location. The simulated operation logic includes unloading operation logic and yard operation logic. Terminal parameters include train arrival time, train arrival interval, train load, belt conveyor speed, yard length, yard width, number of yards, yard stockpile volume, stacker-reclaimer operating efficiency, number of stackers, dumper operating efficiency, and number of dumpers.

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

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

[0110] Step S412: Determine whether the dumper 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 for tipping operation and executes the next step.

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

[0113] Step S421: After the train enters the dumper, the whale optimization algorithm and the fixed-random combination stacking strategy under job balance are used to obtain the stacking location;

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

[0115] Step S423: Determine whether the stacker / reclaimer corresponding to the storage yard is idle. If it is idle, 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, a bulk cargo terminal yard stack allocation system based on simulation optimization is provided, comprising:

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

[0120] The module for obtaining preliminary stacking location allocation results is used to divide the available stacking areas for different bulk cargoes, make preliminary selections of available stacking locations based on the improved whale optimization algorithm, and obtain preliminary stacking location allocation results.

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

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

[0124] In summary, the embodiments of the present invention propose a method and system for allocating stack positions in a bulk cargo terminal yard based on simulation optimization. This scheme takes into account multiple factors such as the yard storage status, bulk cargo type, occupancy of the operation line, 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 additional 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, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or 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 scope of protection of the present invention.

Claims

1. A method for allocating stacking space in bulk cargo terminal yard based on simulation optimization, characterized in that: The following steps are involved: Obtain train information and yard storage status information at the port terminal; Divide the available stacking areas for different bulk cargoes, make a preliminary selection of available stacking locations based on the improved whale optimization algorithm, and obtain preliminary stacking location allocation results; Adjust the preliminary stacking location allocation results based on the fixed-random combination stacking strategy under job balance to determine the final stacking location; including: For bulk cargo types, determining whether the bulk cargo types are high-turnover cargo types or low-turnover cargo types; For high-turnover cargo, the ratio of the occupied area of ​​each storage area to the area of ​​the allocated storage area is calculated based on the occupancy of the storage area, and the yard with the lowest occupancy rate is found. If the initial storage location obtained by the whale optimization algorithm is in the yard, the initial storage location will be the final storage location. If not, the storage areas of the bulk cargo in the yard will be traversed to obtain all available vacant storage locations. If the goods are of low turnover, the system will search the storage areas designated for the goods of low turnover to find out whether the goods of low turnover exist in the storage areas. If there are low-turnover cargoes, the adjacent positions of the low-turnover cargo stacks are traversed to determine whether there are available free stacks. If so, the occupancy rates of the available free stacks are calculated, and the calculated occupancy rates are sorted to find the stack with the lowest occupancy rate. If the initial stacking location is in the yard with the lowest occupancy rate, the initial stacking location will be the stacking location; If the initial stacking location is not in the yard with the lowest occupancy rate, the vacant stacking location adjacent to the stacking location of the low-turnover cargo will be the stacking location for the bulk cargo; If there is no low-turnover cargo, all available storage areas are traversed to find all available stacking locations. Storage space is randomly selected from all available stacking locations, which is the bulk cargo storage location. 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 space in bulk cargo terminal yard 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 status in the waiting area and train model; The storage yard status includes: the type of bulk cargo in the yard, the occupancy of the yard operation line, the specific stacking location of the stacked bulk cargo and the location of the vacant stacking space.

3. The method for allocating stack space in bulk cargo terminal yard 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 in previous years, and calculate the proportion of each bulk cargo turnover in the total turnover; Sort the bulk cargo turnover ratio by size, and divide the bulk cargo into high-turnover bulk cargo and low-turnover bulk cargo according to a certain ratio, so as to obtain the type information of high-turnover bulk cargo and low-turnover bulk cargo; Grid the storage yard, subdivide it into small grids with fixed volumes, and determine the number of grids in each yard based on 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 terminal; for cargoes with small turnover, the yard is divided proportionally in the area far away from the front of the terminal.

4. The method for allocating stack space in bulk cargo terminal yard based on simulation optimization according to claim 1, characterized in that: The improved whale optimization algorithm is used to preliminarily select the stacking locations that can be stored, and the preliminary stacking location allocation results obtained include: Initialize parameters, determine the individual dimension D, the search space range [lb, ub], the number of whales N and the maximum number of iterations T; Determine the encoding and decoding methods for individual whales, initialize the population based on the cargo types and arrival order of the trains, and calculate the individual fitness values ​​of the whale population with the goal of maximizing yard space utilization; Calculate the fitness value of each whale according to the fitness function and find the most well-fed whale; Update the whale's position and introduce adaptive inertia weights 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 stacking 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. The formula is as follows: （1） 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: （2） （3） In the above formula, X pre Indicates 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, and A is a coefficient used to control the range of movement of the whale. , 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 position update formula (2) is used to search the target position globally; when |A|<1, the position update formula (3) is used for local optimization to explore the optimal position of the target; 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 space in bulk cargo terminal yard based on simulation optimization according to claim 1, characterized in that: If there is an available vacant stacking position among all the available stacking positions, then the available space set of the vacant 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 already stacked positions is the storage location for low-turnover cargo; If there is no available stack position, the initial stacking stack position will be the final stacking location.

7. The method for allocating stacking space in bulk cargo terminal yard based on simulation optimization according to claim 1, characterized in that: The simulation layout includes: obtaining overall terminal layout information based on the terminal being studied; wherein the overall terminal layout information includes yard size, number of yards, operation line layout and dumper location; 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 volume, stacker-reclaimer operating efficiency, number of stackers, dumper operating efficiency and number of dumpers.

8. The method for allocating stack 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 for tipping and executes the yard operation logic.

9. A bulk cargo terminal yard stack allocation system based on simulation optimization, characterized in that: include: Data acquisition module, used to obtain train information and yard storage status information at the port terminal; The module for obtaining preliminary stacking location allocation results is used to divide the available stacking areas for different bulk cargoes, make preliminary selections of available stacking locations based on the improved whale optimization algorithm, and obtain preliminary stacking location allocation results. The final stacking location acquisition module is used to adjust the preliminary stacking location allocation results based on the fixed-random combination stacking strategy under job balance to determine the final stacking location; it includes: For bulk cargo types, determining whether the bulk cargo types are high-turnover cargo types or low-turnover cargo types; For high-turnover cargo, the ratio of the occupied area of ​​each storage area to the area of ​​the allocated storage area is calculated based on the occupancy of the storage area, and the yard with the lowest occupancy rate is found. If the initial storage location obtained by the whale optimization algorithm is in the yard, the initial storage location will be the final storage location. If not, the storage areas of the bulk cargo in the yard will be traversed to obtain all available vacant storage locations. If the goods are of low turnover, the system will search the storage areas designated for the goods of low turnover to find out whether the goods of low turnover exist in the storage areas. If there are low-turnover cargoes, the adjacent positions of the low-turnover cargo stacks are traversed to determine whether there are available free stacks. If so, the occupancy rates of the available free stacks are calculated, and the calculated occupancy rates are sorted to find the stack with the lowest occupancy rate. If the initial stacking location is in the yard with the lowest occupancy rate, the initial stacking location will be the stacking location; If the initial stacking location is not in the yard with the lowest occupancy rate, the vacant stacking location adjacent to the stacking location of the low-turnover cargo will be the stacking location for the bulk cargo; If there is no low-turnover cargo, all available storage areas will be traversed to find all available stacking locations, and storage space will be randomly selected from all available stacking locations, which will be the bulk cargo storage locations; 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, and use the simulation model to run the stacking location allocation method to obtain the bulk cargo storage plan.

Images