A method and device for matching plates for whole vehicle logistics transportation

Abstract

The present application relates to the whole vehicle logistics transportation technical field, especially point to a kind of for whole vehicle logistics transportation's board method and device.The method includes: obtaining order data, commodity car data, to be matched board transport capacity data;According to the order data, the commodity car data and the to-be-matched board transport capacity data, determine constraint rule;Using simulated annealing algorithm, the order data, the commodity car data and the to-be-matched board transport capacity data are calculated, determine board scheme;Using the constraint rule, the board scheme is verified, obtain the best board scheme.The above scheme of the present application, using simulated annealing algorithm, order data, commodity car data and to-be-matched board transport capacity data are calculated, determine board scheme, effectively save the time consumed by manual board, it is favorable to improve the shipment full board rate of single transport vehicle, improve board efficiency.

Description

Technical Field

[0001] This invention relates to the field of vehicle logistics transportation technology, and in particular to a method and apparatus for assembling panels for vehicle logistics transportation. Background Technology

[0002] Currently, the common process for assembling vehicle parts is as follows: based on orders, once a truckload is ready in a designated area, the logistics department loads it and ships it to 4S stores across the country. Whole vehicle logistics uses the entire vehicle as the logistics service object, responding quickly and delivering on time according to customer order requirements regarding delivery period, delivery location, and quality assurance. Currently, whole vehicle logistics transportation largely relies on manual planning and assembly of parts. In actual operation, repeated communication is often required to arrive at a relatively reasonable shipment combination. This manual method of vehicle part assembly lacks consideration of multi-dimensional factors, relying heavily on human experience and subjective judgment, resulting in long assembly times and low efficiency. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and apparatus for palletizing for whole vehicle logistics transportation, so as to solve the problems of long time consumption and low efficiency of the existing manual palletizing.

[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0005] A method for palletizing for whole vehicle logistics transportation, comprising:

[0006] Obtain optimal order data, vehicle data, and pallet capacity data;

[0007] Based on the optimal order data, the vehicle data, and the pallet capacity data to be allocated, the constraint rules are determined;

[0008] The simulated annealing algorithm is used to calculate the order data, the vehicle data, and the pallet capacity data to be allocated, and to determine the pallet allocation plan.

[0009] The panel arrangement scheme is verified using the aforementioned constraint rules to obtain the optimal panel arrangement scheme.

[0010] Furthermore, obtain optimal order data, including:

[0011] Obtain the original order data;

[0012] The original order data is sorted according to a preset priority to obtain the optimal order data.

[0013] Further, based on the optimal order data, the vehicle data, and the pallet capacity data to be allocated, constraint rules are determined, including:

[0014] Based on the optimal order data, determine the destination information;

[0015] Based on the vehicle data and the pallet capacity data, the carrying information is determined;

[0016] Based on the destination information and the carrier information, constraint rules are determined.

[0017] Furthermore, the simulated annealing algorithm is used to calculate the order data, the vehicle data, and the palletizing capacity data to determine the palletizing scheme, including:

[0018] The minimum loading height is obtained by using the simulated annealing algorithm to calculate the order data, the vehicle data, and the pallet capacity data to be matched.

[0019] The order data, the vehicle data, and the pallet capacity data to be allocated are calculated using a destructive reconstruction algorithm to obtain a single pallet allocation scheme.

[0020] Based on the minimum loading height and the single-board matching scheme, the board matching scheme is determined.

[0021] Furthermore, the board arrangement scheme is verified using the aforementioned constraint rules to obtain the optimal board arrangement scheme, including:

[0022] Obtain preset line data;

[0023] The pre-defined line data and the constraint rules are used to verify the board configuration scheme to obtain the optimal board configuration scheme.

[0024] Furthermore, the pre-defined line data and the constraint rules are used to verify the board configuration scheme to obtain the optimal board configuration scheme, specifically:

[0025] If the orders in the board matching scheme have the same origin and the board matching scheme meets the constraint rules, then the board matching scheme is the optimal board matching scheme.

[0026] Furthermore,

[0027] The original order data includes: vehicle identification number (VIN), brand information, order identification information, order generation time information, warehouse information, and dealer information;

[0028] The vehicle data includes: length information, width information, height information, weight information, wheelbase information, front overhang information, and rear overhang information of the vehicle;

[0029] The data on the capacity of the car carrier to be matched includes: car carrier vehicle model information, load information, origin information, and destination information.

[0030] Furthermore, the car carrier models in the data on the capacity to be matched are all the same.

[0031] Another aspect of the present invention provides a panel assembly device for whole vehicle logistics transportation, comprising:

[0032] The acquisition module is used to acquire optimal order data, vehicle data, and pallet capacity data, and send them to the constraint module and pallet allocation module.

[0033] The constraint module is used to determine constraint rules based on the optimal order data, the vehicle data, and the pallet capacity data to be allocated, and then send them to the pallet allocation module.

[0034] The pallet allocation module is used to calculate the order data, the vehicle data, and the pallet capacity data to be allocated using a simulated annealing algorithm, determine the pallet allocation scheme, and send it to the verification module.

[0035] The verification module is used to verify the board arrangement scheme using the constraint rules to obtain the optimal board arrangement scheme.

[0036] The above-described solution of the present invention has at least the following beneficial effects:

[0037] The above-mentioned solution of the present invention uses simulated annealing algorithm to calculate order data, vehicle data and pallet capacity data to determine the pallet allocation scheme, which effectively saves the time spent on manual pallet allocation, and helps to improve the full pallet rate of a single transport vehicle and improve pallet allocation efficiency. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating the steps of the plate-assembly method for whole vehicle logistics transportation according to the present invention;

[0039] Figure 2 This is a schematic diagram of the panel assembly device for whole vehicle logistics transportation according to the present invention. Detailed Implementation

[0040] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0041] like Figure 1 As shown, an embodiment of the present invention proposes a palletizing method for whole vehicle logistics transportation, comprising:

[0042] Step S1: Obtain optimal order data, vehicle data, and pallet capacity data;

[0043] Step S2: Determine the constraint rules based on the optimal order data, vehicle data, and pallet capacity data to be allocated;

[0044] Step S3: Use the simulated annealing algorithm to calculate the order data, vehicle data, and pallet capacity data to determine the pallet allocation plan;

[0045] Step S4: Verify the panel layout scheme using constraint rules to obtain the optimal panel layout scheme.

[0046] The above-mentioned solution of the present invention uses simulated annealing algorithm to calculate order data, vehicle data and pallet capacity data to determine the pallet allocation scheme, which effectively saves the time spent on manual pallet allocation, and helps to improve the full pallet rate of a single transport vehicle and improve pallet allocation efficiency.

[0047] In specific implementation, step S1 includes:

[0048] Step S11: Obtain the original order data;

[0049] Step S12: Sort multiple orders in the original order data according to preset priorities to obtain the optimal order data.

[0050] The original order data includes: vehicle identification number (VIN), brand information, order identification information, order generation time information, warehouse information, and dealer information;

[0051] The data for the finished vehicle includes: length, width, height, weight, wheelbase, front overhang, and rear overhang information.

[0052] The data on the available car carrier capacity includes: car carrier vehicle model information, load information, origin information, and destination information.

[0053] The preset priority can be: Customer Order > Hongqi Brand > Order Generation (or Issuance) Time. Orders with higher priority are processed first, which can meet special user needs and increase adaptability. The Hongqi brand can also be changed to other brands according to user needs. This embodiment is only for illustrative purposes and does not limit the scope of protection of this invention.

[0054] The data on available capacity also includes individual reports from transporters regarding transportation demand gaps in certain destination cities. When allocating capacity, priority should be given to matching the reported capacity. The information includes order number, required quantity, origin city, and a list of destination cities.

[0055] When allocating pallets for a route, a full pallet is defined as all car carrier bays filled with cars. Similarly, a full pallet is also defined as the number of vehicles filling all available spaces to meet the capacity requested by the transporter. The route fullness rate is calculated as follows: Route fullness rate = (Number of full pallets on that route) / Total number of cars. The overall fullness rate is calculated across the entire route: Overall fullness rate = (Number of full pallets on all routes) / Total number of cars on all routes. Sorting orders can help improve the fullness rate.

[0056] In specific implementation, step S2 includes:

[0057] Step S21: Determine the destination information based on the optimal order data;

[0058] Step S22: Determine the carrying capacity information based on the vehicle data and the pallet capacity data to be matched;

[0059] Step S23: Determine the constraint rules based on the destination information and the carrier information.

[0060] The order contains warehouse and distributor information, with the warehouse as the origin and the distributor and its city as the destination. All destinations in the order are extracted, and then constraints are established: the number of cities where the single pallet arrives at its destination is less than or equal to 2, and the number of distributors arriving within the same city is less than or equal to 2. The palletizing capacity data includes car carrier vehicle model and load information, which are used to determine constraints such as car carrier load capacity, parking space size, spacing, and height limit. The purpose of these constraints is to improve the clustering and scientific nature of palletizing. Clustering is divided into single-pallet clustering and overall clustering. Better clustering of orders for a particular pallet in the palletizing scheme means lower transportation costs, less unloading time, and less unloading operation difficulty. Single-pallet clustering can be measured by clustering indicators, involving the number of cargo points, the number of destination cities, the average distance between destinations, and the number of brands. The number of distributors, cities, distances, and brands are compared level by level; lower values ​​indicate better clustering. Overall clustering is determined by the sum of the single-pallet clustering in the palletizing results.

[0061] In specific implementation, step S3 includes:

[0062] Step S31: Use the simulated annealing algorithm to calculate the minimum loading height based on the order data, vehicle data, and pallet capacity data.

[0063] Step S32: Use the destructive reconstruction algorithm to calculate the order data, vehicle data, and pallet capacity data to obtain the single-pallet allocation scheme;

[0064] Step S33: Determine the panel arrangement scheme based on the minimum loading height and the single-board panel arrangement scheme.

[0065] One level of a car carrier is called a single deck, and a single deck can carry multiple cars.

[0066] Simulated annealing is a metaheuristic acceptance strategy. Its calculation process is as follows: the initial temperature of the system is T0, and after f iterations, the final temperature is T0. f In each iteration, the temperature is reduced using a cooling constant c. Therefore, the cooling constant c can be calculated using the following formula:

[0067]

[0068] Accept neighborhood solution s * The probability h depends on the current temperature T and s * The energy difference ΔE with the current solution s = cost(s) * )-cost(s),

[0069] As can be seen from the above acceptance criteria, when the cost of the neighboring solution is less than the cost of the current solution, it will definitely be accepted, while when the cost of the neighboring solution is worse than the current solution, it may be accepted with some probability.

[0070] When performing a neighborhood search, very large neighborhoods often appear when a major part of the solution is modified, making the entire neighborhood exploration impractical. Therefore, it is advisable to start from a smaller candidate set. In this process, a neighborhood can be selected, or a single neighborhood can be generated by first destroying the current solution and then reconstructing it into a feasible solution. Such methods can be represented by the destruction-reconstruction algorithm R- and the recreate algorithm R+, abbreviated as R&R.

[0071] When a vehicle is removed from a platform, several relaxations may be introduced: weight relaxation, which releases the vehicle's load capacity; spatial relaxation, which releases the vehicle's space capacity; and clustering relaxation, which allows for an improvement in vehicle clustering. An effective R-method should incorporate these relaxations into the solution, greatly increasing the likelihood of an improved solution. To avoid failure due to relaxations, the solution should possess the following characteristics:

[0072] Remove "grouped" vehicles from the same pallet truck. For example, vehicles in positions (1,2), (3,4), (5,6), and (7,8) of vehicle Y, which are also dealer vehicles, may not provide sufficient capacity slack if "ungrouped" vehicles are removed.

[0073] Different sections retain "similar" vehicles, from the same dealer and the same city.

[0074] Remove vehicles with significant differences between different sections, such as those with large size differences, those from different dealers, or those from different cities.

[0075] Removed vehicles according to order

[0076] Group and sort by number, distributor, city, and size.

[0077] Remove the order from the configuration sheet according to the rules;

[0078] Orders with the same number should be removed together;

[0079] Parking spaces that are close together should be removed together;

[0080] There is a chance that the same dealer will be removed together;

[0081] Orders from the same city may be removed together;

[0082] Remove the opposite position;

[0083] The vacated parking spaces are cleaned to restore the original structure of the panels.

[0084] Each car is inserted into an empty space in turn, and the constraints are checked. The positions that meet the constraints are scored. After all positions have been traversed, the position with the highest score is selected for insertion.

[0085] The reconstruction algorithm optimizes the order order in the pallet by reconstructing pallets and changing the order of unassigned orders based on different sorting methods. First, unassigned orders are randomly sorted using one method. Then, each order is attempted to be inserted into an existing submission pallet or pallet to optimize the objective function. If no suitable pallet is found, a new pallet is created and the order is inserted into it. The overall goal is to optimize palletizing, ensuring that unassigned orders are assigned to the most appropriate positions to optimize the objective function. The insertion order of vehicles is determined by selecting one of several sorting methods: random, order priority, vehicle height, same dealer, same city, same brand, number of order groups with the same order number, and each order attempts to insert into all existing pallets, checking all constraints to find the pallet that best achieves clustering. If no pallet can be used, a new pallet is created and the order is inserted into it.

[0086] In practice, step S1 also includes: acquiring preset line data;

[0087] Step S4 further includes: verifying the board matching scheme using preset line data and constraint rules. If the orders in the board matching scheme have the same starting point and the board matching scheme meets the constraint rules, then the board matching scheme is the best board matching scheme.

[0088] The preset routes are several routes from the origin to the destination, forming a batch of orders. Orders must be placed within these preset routes, with the origin city being the same, while multiple destination cities are allowed. The simulated annealing algorithm finds the optimal route during computation. The algorithm iterates through loops, executing a destructive reconstruction algorithm to create new candidate routes during each iteration. It then decides whether to accept a candidate route based on acceptance criteria. If a candidate route is accepted, it becomes the current route; if it is the best route found so far, it becomes the optimal route. The temperature value is updated after each iteration by decreasing its cooling constant c.

[0089] In practice, the car carrier models in the data to be matched for palletizing are all of the same type. Matching car carriers of the same model to palletizing improves palletizing efficiency.

[0090] like Figure 2 This embodiment provides a panel assembly device for whole vehicle logistics transportation, comprising:

[0091] The acquisition module is used to acquire optimal order data, vehicle data, and pallet capacity data, and send them to the constraint module and pallet allocation module.

[0092] The constraint module is used to determine constraint rules based on the optimal order data, the vehicle data, and the pallet capacity data to be allocated, and then send them to the pallet allocation module.

[0093] The pallet allocation module is used to calculate the order data, the vehicle data, and the pallet capacity data to be allocated using a simulated annealing algorithm, determine the pallet allocation scheme, and send it to the verification module.

[0094] The verification module is used to verify the board arrangement scheme using the constraint rules to obtain the optimal board arrangement scheme.

[0095] The above-mentioned solution of the present invention uses simulated annealing algorithm to calculate order data, vehicle data and pallet capacity data to determine the pallet allocation scheme, which effectively saves the time spent on manual pallet allocation, and helps to improve the full pallet rate of a single transport vehicle and improve pallet allocation efficiency.

[0096] In practical implementation, the acquisition module is specifically used for:

[0097] Obtain the original order data;

[0098] The original order data is sorted according to a preset priority to obtain the optimal order data.

[0099] The original order data includes: vehicle identification number (VIN), brand information, order identification information, order generation time information, warehouse information, and dealer information;

[0100] The data for the finished vehicle includes: length, width, height, weight, wheelbase, front overhang, and rear overhang information.

[0101] The data on the available car carrier capacity includes: car carrier vehicle model information, load information, origin information, and destination information.

[0102] The preset priority can be: Customer Order > Hongqi Brand > Order Generation (or Issuance) Time. Orders with higher priority are processed first, which can meet special user needs and increase adaptability. The Hongqi brand can also be changed to other brands according to user needs. This embodiment is only for illustrative purposes and does not limit the scope of protection of this invention.

[0103] In practical implementation, the constraint module is specifically used for:

[0104] Determine destination information based on optimal order data;

[0105] Based on the data of the finished vehicles and the available pallet capacity, the carrying information is determined;

[0106] Based on the destination information and the carrier information, determine the constraint rules.

[0107] The order contains warehouse and distributor information, with the warehouse as the origin and the distributor and its city as the destination. All destinations in the order are extracted, and then constraints are established: the number of cities where the single pallet arrives at its destination is less than or equal to 2, and the number of distributors arriving within the same city is less than or equal to 2. The palletizing capacity data includes car carrier vehicle model and load information, which are used to determine constraints such as car carrier load capacity, parking space size, spacing, and height limit. The purpose of these constraints is to improve the clustering and scientific nature of palletizing. Clustering is divided into single-pallet clustering and overall clustering. Better clustering of orders for a particular pallet in the palletizing scheme means lower transportation costs, less unloading time, and less unloading operation difficulty. Single-pallet clustering can be measured by clustering indicators, involving the number of cargo points, the number of destination cities, the average distance between destinations, and the number of brands. The number of distributors, cities, distances, and brands are compared level by level; lower values ​​indicate better clustering. Overall clustering is determined by the sum of the single-pallet clustering in the palletizing results.

[0108] In practical implementation, the board module is specifically used for:

[0109] The minimum loading height is calculated using simulated annealing algorithm on order data, vehicle data, and pallet capacity data.

[0110] The destructive reconstruction algorithm is used to calculate the single-board allocation scheme by analyzing the order data, the vehicle data, and the pallet capacity data.

[0111] The panel arrangement scheme is determined based on the minimum loading height and the single-board panel arrangement scheme.

[0112] One level of a car carrier is called a single deck, and a single deck can carry multiple cars.

[0113] Simulated annealing is a metaheuristic acceptance strategy. Its calculation process is as follows: the initial temperature of the system is T0, and after f iterations, the final temperature is T0. f In each iteration, the temperature is reduced using a cooling constant c. Therefore, the cooling constant c can be calculated using the following formula:

[0114]

[0115] Accept neighborhood solution s * The probability h depends on the current temperature T and s * The energy difference ΔE with the current solution s = cost(s) * )-cost(s),

[0116] As can be seen from the above acceptance criteria, when the cost of the neighboring solution is less than the cost of the current solution, it will definitely be accepted, while when the cost of the neighboring solution is worse than the current solution, it may be accepted with some probability.

[0117] When performing a neighborhood search, a very large neighborhood often appears when a major part of the solution is modified, making the entire neighborhood exploration impractical. Therefore, it is advisable to start from a smaller candidate set. In this process, a neighborhood can be selected, or a single neighborhood can be generated by first destroying the current solution and then reconstructing it into a feasible solution. Such methods can be represented by the destruction-reconstruction algorithm R- and the recreate algorithm R+, abbreviated as R&R.

[0118] When a vehicle is removed from a platform, several relaxations may be introduced: weight relaxation, which releases the vehicle's load capacity; spatial relaxation, which releases the vehicle's space capacity; and clustering relaxation, which allows for an improvement in vehicle clustering. An effective R-method should incorporate these relaxations into the solution, greatly increasing the likelihood of an improved solution. To avoid failure due to relaxations, the solution should possess the following characteristics:

[0119] Remove "grouped" vehicles from the same pallet truck. For example, vehicles in positions (1,2), (3,4), (5,6), and (7,8) of vehicle Y, which are also dealer vehicles, may not provide sufficient capacity slack if "ungrouped" vehicles are removed.

[0120] Different sections retain "similar" vehicles, from the same dealer and the same city.

[0121] Remove vehicles with significant differences between different sections, such as those with large size differences, those from different dealers, or those from different cities.

[0122] The removed vehicles were grouped and sorted according to their Y-number, same dealer, same city, and same size.

[0123] Each car is inserted into an empty space in turn, and the constraints are checked. The positions that meet the constraints are scored. After all positions have been traversed, the position with the highest score is selected for insertion.

[0124] If there is no available space to insert, create a patch.

[0125] In practice, the acquisition module is also used to: acquire preset line data;

[0126] The verification module is also used to: verify the board matching scheme using preset line data and constraint rules. If the orders in the board matching scheme have the same starting point and the board matching scheme meets the constraint rules, then the board matching scheme is the best board matching scheme.

[0127] The preset routes are several routes from the origin to the destination, forming a batch of orders. Within the preset routes, the origin city must be the same, while there can be multiple destination cities.

[0128] In practice, the car carrier models in the data to be matched for palletizing are all of the same type. Matching car carriers of the same model to palletizing improves palletizing efficiency.

[0129] Embodiments of the present invention also provide a computer-readable storage medium storing instructions thereon that, when executed on a computer, cause the computer to perform actions such as Figure 1 The method described herein, and other embodiments thereof, will not be described in detail.

[0130] The palletizing method and apparatus for full-truckload logistics transportation of this invention employs simulated annealing and a destruction and reconstruction (R&R) strategy algorithm. Tailored to the characteristics of full-truckload transportation, it achieves compliant loading and multi-brand mixed loading, improves highway load factor and timely allocation rate, reduces overall transportation costs, and ensures transportation quality. The method and apparatus consider factors such as order priority, selectable preset routes, and clustering, and based on a series of constraint rules, maximizes the number of full pallets in palletizing, minimizes the total number of pallets in palletizing, maximizes the priority of full-pallet orders, and minimizes overall clustering, thereby optimizing the full-truckload transportation plan.

[0131] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for palletizing for whole vehicle logistics transportation, characterized in that, include: Obtain optimal order data, vehicle data, and pallet capacity data; Based on the optimal order data, the vehicle data, and the pallet capacity data to be allocated, the constraint rules are determined; The simulated annealing algorithm is used to calculate the order data, the vehicle data, and the pallet capacity data to be allocated, and to determine the pallet allocation plan. The optimal board arrangement scheme is obtained by verifying the board arrangement scheme using the aforementioned constraint rules. Obtaining optimal order data includes: Obtain the original order data; The original order data is sorted according to a preset priority to obtain the optimal order data; Among them, constraint rules are determined based on the optimal order data, the vehicle data, and the pallet capacity data to be allocated, including: Based on the optimal order data, determine the destination information; Based on the vehicle data and the pallet capacity data, the carrying information is determined; Based on the destination information and the carrier information, the constraint rules are determined; The process includes using simulated annealing algorithm to calculate the order data, the vehicle data, and the palletizing capacity data to determine the palletizing scheme, including: The minimum loading height is obtained by using the simulated annealing algorithm to calculate the order data, the vehicle data, and the pallet capacity data to be matched. The order data, the vehicle data, and the pallet capacity data to be allocated are calculated using a destructive reconstruction algorithm to obtain a single pallet allocation scheme. Based on the minimum loading height and the single-board matching scheme, the board matching scheme is determined.

2. The palletizing method for whole vehicle logistics transportation according to claim 1, characterized in that, The board arrangement scheme is verified using the aforementioned constraint rules to obtain the optimal board arrangement scheme, including: Obtain preset line data; The pre-defined line data and the constraint rules are used to verify the board configuration scheme to obtain the optimal board configuration scheme.

3. The palletizing method for whole vehicle logistics transportation according to claim 2, characterized in that, The optimal board configuration scheme is obtained by verifying the preset line data and the constraint rules. Specifically: If the orders in the board matching scheme have the same origin and the board matching scheme meets the constraint rules, then the board matching scheme is the optimal board matching scheme.

4. The palletizing method for whole vehicle logistics transportation according to claim 1, characterized in that, The original order data includes: vehicle identification number (VIN), brand information, order identification information, order generation time information, warehouse information, and dealer information; The vehicle data includes: length information, width information, height information, weight information, wheelbase information, front overhang information, and rear overhang information of the vehicle; The data on the capacity of the car carrier to be matched includes: car carrier vehicle model information, load information, origin information, and destination information.

5. The palletizing method for whole vehicle logistics transportation according to claim 4, characterized in that, The car carrier models in the data on the capacity to be matched are all of the same type.

6. A palletizing device for whole vehicle logistics transportation, characterized in that, include: The acquisition module is used to acquire optimal order data, vehicle data, and pallet capacity data, and send them to the constraint module and pallet allocation module. The constraint module is used to determine constraint rules based on the optimal order data, the vehicle data, and the pallet capacity data to be allocated, and then send them to the pallet allocation module. The pallet allocation module is used to calculate the order data, the vehicle data, and the pallet capacity data to be allocated using a simulated annealing algorithm, determine the pallet allocation scheme, and send it to the verification module. The verification module is used to verify the board arrangement scheme using the constraint rules to obtain the optimal board arrangement scheme; The process of obtaining optimal order data includes: obtaining original order data; sorting multiple orders in the original order data according to a preset priority to obtain optimal order data; The process of determining constraint rules based on the optimal order data, the vehicle data, and the pallet capacity data includes: determining destination information based on the optimal order data; determining carrying information based on the vehicle data and the pallet capacity data; and determining constraint rules based on the destination information and the carrying information. The process of using simulated annealing algorithm to calculate the order data, vehicle data, and pallet capacity data to determine the pallet allocation scheme includes: using simulated annealing algorithm to calculate the minimum loading height; using destructive reconstruction algorithm to calculate the single-pallet allocation scheme; and determining the pallet allocation scheme based on the minimum loading height and the single-pallet allocation scheme.

7. A computer-readable storage medium, characterized in that, The computer stores instructions that, when executed on the computer, cause the computer to perform the method as described in any one of claims 1 to 5.

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