Emergency material vehicle cargo matching and stowage optimization method and device based on two-stage mixed taboo search and medium

Abstract

The invention discloses an emergency material vehicle cargo matching and stowage optimization method and device based on two-stage mixed taboo search and a medium, and the method comprises the steps: building a cargo stowage and vehicle path comprehensive transportation model, and the composition factors in the comprehensive transportation model comprise loading space, loading capacity and road section traffic capacity; inputting an emergency material transportation task set in the planned time period, wherein information contained in each transportation task comprises a starting place, a destination place, a cargo quantity, a cargo volume and a cargo weight of each transportation task; constructing an alternative path set by adopting a genetic algorithm according to the comprehensive transportation model and the emergency material transportation task; and in consideration of transport tasks, the number of vehicles and transport capacity constraint conditions, a tabu search algorithm is adopted to solve an emergency material transport optimization scheduling problem, and an optimal scheduling scheme is selected from the alternative path set. The cost is reduced, the logistics efficiency is improved, and a feasible solution is provided for the cargo transportation stowage problem.

Description

Technical Field

[0001] The present invention relates to the field of logistics management, and in particular to a method, device and medium for optimizing vehicle-cargo matching and loading of emergency materials based on a two-stage hybrid taboo search. Background Art

[0002] Genetic algorithm: It is a heuristic search and optimization algorithm that simulates the genetic mechanism in the process of biological evolution. Through operations such as gene expression, selection, crossover and mutation of candidate solutions, it evolves solutions with higher fitness from generation to generation, and is used to solve optimization problems, such as searching for the optimal solution and optimizing parameter configuration.

[0003] Taboo search: It is a heuristic search method used to solve optimization problems. It avoids repeatedly visiting the same solution or falling into the local optimal solution during the search process by maintaining a short-term memory structure taboo table, thereby exploring a wider solution space.

[0004] GA-TS (Genetic Algorithm-Tabu Search): It is an optimization algorithm that combines genetic algorithm and taboo search. By combining the two, it can perform both extensive global search and more refined optimization locally, thus balancing the advantages of global search and local search.

[0005] Emergency material transportation refers to a special logistics activity that provides emergency support after serious natural disasters, sudden public health incidents and other emergencies. Compared with traditional logistics and transportation methods, the particularity of emergency material transportation requires the system to respond quickly and efficiently in emergency situations. Loading optimization is a key task to improve resource utilization efficiency, reduce costs and optimize logistics operations. In the optimization of emergency material transportation scheduling, this task is particularly complex and urgent, and is of vital importance to improving overall logistics efficiency and responding to market demand.

[0006] Load optimization aims to reasonably allocate goods to transportation vehicles under various constraints to reduce costs, shorten transportation time, and ensure the safety and timely delivery of goods. A key challenge in this field is to develop a transportation load plan with vehicle-cargo matching function considering the finiteness of resources and multiple constraints. This involves intelligently selecting appropriate transportation vehicles, determining the optimal loading quantity of goods, and formulating reasonable transportation routes. This process needs to fully consider various factors, such as the characteristics of goods, the capacity of transportation vehicles, the traffic capacity of routes, etc. To address this challenge, it is crucial to develop a load optimization method based on vehicle-cargo matching function. This algorithm needs to combine advanced optimization techniques to formulate the best load plan according to the actual situation. The core of the algorithm is to search for the optimal solution under various constraints to achieve maximum benefit. This involves using mathematical models to model the problem and using algorithms to search for the best solution. This load optimization method based on optimization algorithm will bring significant benefits to various logistics activities. It can greatly reduce logistics costs, improve transportation efficiency, and also help reduce environmental impact. With the further development of technology, this method can be continuously optimized and improved to provide a more intelligent and efficient solution for logistics management. Whether in the traditional logistics field or in the field of emergency material transportation, this method has broad application prospects. Summary of the Invention

[0007] Aiming at the deficiencies in the prior art, the present invention provides an emergency material vehicle-cargo matching and load optimization method, device and medium based on two-stage hybrid tabu search to optimize the transportation loading plan of emergency material transportation tasks. The present invention establishes a joint optimization model for vehicle-cargo matching and load optimization. It transforms the multi-objective problem with the maximum load rate and the minimum mileage into a single-objective function with the lowest cost, and improves the overall efficiency of emergency material transportation from a global perspective.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] An emergency material vehicle-cargo matching and load optimization method based on two-stage hybrid tabu search, comprising the following steps:

[0010] S1. Build a comprehensive transportation model for cargo loading and vehicle routing. The constituent factors in the comprehensive transportation model include loading space, loading capacity, and section traffic capacity;

[0011] S2. Input the set of emergency material transportation tasks within the planned period. The information included in each transportation task includes the starting location, destination location, quantity of goods, volume of goods, and weight of goods for each transportation task;

[0012] S3. Based on the integrated transportation model established in step S1 and the emergency material transportation tasks obtained in step S2, use the genetic algorithm to construct a set of alternative paths; considering the transportation tasks, the number of vehicles, and the transportation capacity constraints, use the tabu search algorithm to solve the emergency material transportation optimization scheduling problem, and select the optimal scheduling plan from the set of alternative paths.

[0013] To optimize the above technical solutions, the specific measures taken also include:

[0014] Further, in S1, the specific method of establishing the integrated transportation model of cargo loading and vehicle routing is as follows:

[0015] Use a directed graph G = (A, E) to represent the transportation network, where the set of stations is A = {a0, a1, …, a n}, where a0 is the parking lot, and a n is the nth station, and the set of road segments is E = {(a ij , …): i ≠ j}, and a ij represents the road segment between the ith station and the jth station; there is a transportation queue composed of m different types of transportation tools in the parking lot. The transportation tool k has a maximum load-bearing capacity G, a cost C, and a three-dimensional loading space with length L, width W, and height H. The volume of the loading space V = L * H * W. Let the transportation tool k = (1, …, m), and the cost of moving from the ith station to the jth station is where d ij is the distance from station i to station j, c2 represents the transportation cost, m is the total number of transportation tools, n is the total number of stations, represents the decision variable of whether vehicle k travels from station i to station j. If vehicle k travels from station i to station j, the decision variable otherwise it is 0, and y k represents the decision variable of whether vehicle k is used. If vehicle k is used, it is equal to 1, otherwise it is 0.

[0016] Further, S3 is specifically as follows:

[0017] S3.1. Take the set of emergency material transportation tasks within the planning period as the input. Each task includes the starting location, destination location, quantity, volume, and weight of the goods;

[0018] S3.2. Initialize the genetic algorithm to obtain a population, which contains multiple individuals, and each individual is a transportation and loading plan;

[0019] S3.3. Construct a fitness function based on the total transportation time and the loading rate of the distribution vehicles;

[0020] S3.4. Initialize the global optimal solution S best and the current solution S t, set the maximum number of iterations;

[0021] S3.5. Perform a crossover operation on the selected individuals using the two-point crossover method to obtain offspring individuals, and then perform a mutation operation on the selected individuals to obtain offspring individuals as the alternative path set;

[0022] S3.6. Optimize the offspring individuals using the tabu search algorithm;

[0023] S3.7. Select a pair of destruction operators and repair operators to perform a neighborhood search operation to generate a new solution S new ;

[0024] S3.8. Determine whether the new solution satisfies the aspiration criterion. If it does, use the solution that satisfies the aspiration criterion as the current solution, and replace the object that entered the tabu list earliest with the solution corresponding to the current solution, update the optimal state, and re-execute step S3.6; otherwise, go to step S3.9;

[0025] S3.9. Determine the tabu attribute of the new solution, use the best solution corresponding to the non-tabu object as the current solution, and replace the object that entered the tabu list earliest with the solution corresponding to the current solution;

[0026] S3.10. Compare the fitness function values of the new solution S new and the current solution S t . If the new solution is better than the current solution, directly replace the current solution with the new solution. At the same time, update the global optimal solution S best ; Determine whether the maximum number of iterations has been reached. If so, output the global optimal solution S best as the optimal scheduling plan. Otherwise, return to step S3.7.

[0027] Furthermore, in S3.3, the fitness function is specifically:

[0028]

[0029] where C represents the total scheduling cost, c1 represents the loading cost, c2 represents the transportation cost, where d ij is the distance from site i to site j, m is the total number of transportation tools, n is the total number of sites, represents the decision variable of whether vehicle k travels from site i to site j. If vehicle k travels from site i to site j, the decision variable otherwise it is 0, and y k represents the decision variable of whether vehicle k is used. If vehicle k is used, it is equal to 1, otherwise it is 0;

[0030] The constraint conditions of the fitness function include:

[0031]

[0032]

[0033] Among them, constraints (2) and (3) ensure that the loading route is a closed route. A decision variable indicating that vehicle k travels from the parking lot to station j. A decision variable indicating that vehicle k travels from station i to the parking lot. Constraint (4) limits each station to be visited only once. Constraint (5) ensures that if a vehicle arrives at a station, it must depart from this station to another station. h represents the intermediate station where the vehicle arrives. Constraint (6) restricts that the items placed in the vehicle must be completely within the loading space. Represents the loading position of the items loaded in vehicle k in the x-axis direction of the vehicle. Represents the loading position of the items loaded in vehicle k in the y-axis direction of the vehicle. Represents the loading position of the items loaded in vehicle k in the z-axis direction of the vehicle. L is the maximum loading length of the vehicle in the x-axis direction, W is the maximum loading width of the vehicle in the y-axis direction, and Z is the maximum loading height of the vehicle in the z-axis direction.

[0034] Furthermore, in S3.7, the selection of a pair of destruction operators and repair operators specifically means randomly selecting a pair of destruction operators and repair operators from the following destruction operators and repair operators:

[0035] Destruction operator 1: Randomly select 10% of the tasks and delete their solutions.

[0036] Destruction operator 2: Randomly select 20% of the tasks and delete their solutions.

[0037] Repair operator 1: For all deleted tasks, randomly reset the matching vehicle from the alternative solution set and randomly reset the transportation volume borne by the path.

[0038] Repair operator 2: For all deleted tasks, keep the original transportation vehicle and randomly reset the transportation volume borne by the path.

[0039] The present invention also provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned emergency material vehicle-load matching and stowage optimization method based on two-stage hybrid tabu search.

[0040] The present invention also provides a computer-readable storage medium storing a computer program, and the computer program causes a computer to execute the above-mentioned emergency material vehicle-load matching and stowage optimization method based on two-stage hybrid tabu search.

[0041] The beneficial effects of the present invention are as follows: Aiming at the problem of optimizing the loading and matching of emergency supplies during dispatching, the present invention designs a method, device, and medium for vehicle-cargo matching and loading optimization of emergency supplies based on a two-stage hybrid tabu search to meet the high-efficiency emergency supply transportation requirements under emergencies. Through the hybrid tabu search algorithm, combining global search and local optimization strategies, the algorithm has stronger search capabilities and accuracy. In the first stage, the algorithm fully considers the diversity of solutions and constructs a diverse solution set through means such as crossover and mutation. In the second stage, through the vehicle-cargo matching strategy, various constraints such as transportation routes and vehicle capacities are satisfied to ensure the efficient completion of cargo loading within a limited time. The introduction of the tabu search method effectively solves the multi-constraint loading optimization problem, improves the quality and efficiency of solutions. The application of the algorithm provides reliable technical support for the problem of optimizing the loading and matching of emergency supplies transportation, optimizes resource utilization, reduces costs, improves logistics efficiency, provides a feasible solution for the problem of cargo loading and matching during emergencies, and provides an important impetus for the management level in the field of emergency supplies transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 FIG. is the overall flowchart of the method for vehicle-cargo matching and loading optimization of emergency supplies based on a two-stage hybrid tabu search proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0044] Embodiment 1

[0045] The present invention proposes a method for vehicle-cargo matching and loading optimization of emergency supplies based on a two-stage hybrid tabu search. The overall process of this method is as Figure 1 shown and includes the following steps:

[0046] S1. Build an integrated transportation model for cargo loading and vehicle routing. The components in the integrated transportation model include loading space, loading capacity, and section passing capacity. In S1, the specific method of building the integrated transportation model for cargo loading and vehicle routing is as follows:

[0047] Use a directed graph G=(A, E) to represent the transportation network, where the site set is A={a0, a1,..., a n}, where a0 is the parking lot, and a n is the nth site, and the section set is E={(a ij ,…): i≠j}, aij Denote the road section between the \(i\)-th site and the \(j\)-th site; the parking lot has a transportation queue composed of \(m\) different types of transportation tools. The transportation tool \(k\) has a maximum load-bearing capacity \(G\), a cost \(C\), and a three-dimensional loading space with length \(L\), width \(W\), and height \(H\). The volume of the loading space \(V = L\times H\times W\). The cost of moving the transportation tool \(k=(1,\cdots,m)\) from the \(i\)-th site to the \(j\)-th site is where \(d\) ij is the distance from site \(i\) to site \(j\), \(c_2\) represents the transportation cost, \(m\) is the total number of transportation tools, and \(n\) is the total number of sites. Denote the decision variable indicating whether vehicle \(k\) travels from site \(i\) to site \(j\). If vehicle \(k\) travels from site \(i\) to site \(j\), the decision variable is \(1\), otherwise it is \(0\). \(y\) k Denote the decision variable indicating whether vehicle \(k\) is used. If vehicle \(k\) is used, it is equal to \(1\), otherwise it is \(0\).

[0048] S2. Input the set of emergency material transportation tasks within the planned time period. The information included in each transportation task includes the starting location, destination location, quantity of goods, volume of goods, and weight of goods.

[0049] S3. Based on the integrated transportation model established in step S1 and the emergency material transportation tasks obtained in step S2, construct an alternative path set using the genetic algorithm; considering the transportation tasks, the number of vehicles, and the transportation capacity constraints, use the tabu search algorithm to solve the emergency material transportation optimization scheduling problem and select the optimal scheduling plan from the alternative path set.

[0050] Specifically, S3 is as follows:

[0051] S3.1. Take the set of emergency material transportation tasks within the planned time period as the input. Each task includes the starting location, destination location, quantity of goods, volume, and weight.

[0052] S3.2. Initialize the genetic algorithm to obtain a population, which contains multiple individuals, and each individual is a transportation and loading plan.

[0053] S3.3. Construct a fitness function based on the total transportation time and the loading rate of the distribution vehicle. In S3.3, the specific fitness function is:

[0054]

[0055] where \(C\) represents the total scheduling cost, \(c_1\) represents the loading cost, \(c_2\) represents the transportation cost, where \(d\) ij is the distance from site \(i\) to site \(j\), \(m\) is the total number of transportation tools, and \(n\) is the total number of sites. Denote the decision variable indicating whether vehicle \(k\) travels from site \(i\) to site \(j\). If vehicle \(k\) travels from site \(i\) to site \(j\), the decision variable Otherwise it is 0, y k A decision variable indicating whether vehicle k is used. If vehicle k is used, it is equal to 1; otherwise it is 0;

[0056] The constraint conditions of the fitness function include:

[0057]

[0058] Among them, constraint (2) and constraint (3) ensure that the loading route is a closed route, A decision variable indicating that vehicle k travels from the parking lot to station j, A decision variable indicating that vehicle k travels from station i to the parking lot. Constraint (4) restricts each station to be visited only once. Constraint (5) ensures that if a vehicle arrives at a station, then it must depart from this station to another station. h represents the intermediate station where the vehicle arrives. Constraint (6) restricts that the items placed in the vehicle must be completely within the loading space, Represents the loading position of the item loaded in vehicle k in the x-axis direction of the vehicle, Represents the loading position of the item loaded in vehicle k in the y-axis direction of the vehicle, Represents the loading position of the item loaded in vehicle k in the z-axis direction of the vehicle. L is the maximum loading length in the x-axis direction of the vehicle, W is the maximum loading width in the y-axis direction of the vehicle, and Z is the maximum loading height in the z-axis direction of the vehicle.

[0059] S3.4. Initialize the global optimal solution S best and the current solution S t , and set the maximum number of iterations;

[0060] S3.5. Perform a crossover operation on the selected individuals using the two-point crossover method to obtain offspring individuals, and then perform a mutation operation on the selected individuals to obtain offspring individuals as the alternative path set;

[0061] S3.6. Optimize the offspring individuals using the tabu search algorithm;

[0062] S3.7. Select a pair of destruction operators and repair operators to perform a neighborhood search operation to generate a new solution S new ; In S3.7, the selection of a pair of destruction operators and repair operators specifically means randomly selecting a pair of destruction operators and repair operators from the following destruction operators and repair operators:

[0063] Destruction operator 1: Randomly select 10% of the tasks and delete their solutions;

[0064] Destruction operator 2: Randomly select 20% of the tasks and delete their solutions;

[0065] Repair operator 1: For all deleted tasks, randomly reset the matching vehicles from the set of alternative solutions, and randomly reset the transportation volume borne by the paths.

[0066] Repair operator 2: For all deleted tasks, keep the original transportation vehicles and randomly reset the transportation volume borne by the paths.

[0067] S3.8. Determine whether the new solution satisfies the aspiration criterion. If it does, use the solution that satisfies the aspiration criterion as the current solution, and replace the object that entered the taboo list earliest with the solution corresponding to the current solution, update the optimal state, and re - execute step S3.6; otherwise, go to step S3.9.

[0068] S3.9. Determine the taboo attribute of the new solution, use the best solution corresponding to the non - taboo object as the current solution, and replace the object that entered the taboo list earliest with the solution corresponding to the current solution.

[0069] S3.10. Compare the fitness function values of the new solution S new and the current solution S t . If the new solution is better than the current solution, directly replace the current solution with the new solution. At the same time, update the global optimal solution S best ; Determine whether the maximum number of iterations is reached. If so, output the global optimal solution S best as the optimal scheduling plan. Otherwise, return to step S3.7.

[0070] Embodiment 2

[0071] The present invention provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the emergency material vehicle - cargo matching and loading optimization method based on two - stage hybrid taboo search as described in Embodiment 1.

[0072] Embodiment 3

[0073] The present invention provides a computer - readable storage medium storing a computer program, and the computer program causes a computer to execute the emergency material vehicle - cargo matching and loading optimization method based on two - stage hybrid taboo search as described in Embodiment 1.

[0074] In the embodiments disclosed in the present application, a computer storage medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the computer storage medium would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0075] Those of ordinary skill in the art will appreciate that the units and algorithm steps of the examples described in connection with the embodiments disclosed in the present application can be implemented in electronic hardware or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0076] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art of the present technology, several improvements and refinements made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. An emergency material vehicle-load matching and stowage optimization method based on two-stage hybrid tabu search, characterized in that It includes the following steps: S1. Build an integrated transportation model for cargo loading and vehicle routing. The components of the integrated transportation model include loading space, loading capacity, and road section passing capacity; S2. Input the set of emergency material transportation tasks within the planned period. The information included in each transportation task includes the starting location, destination location, quantity of goods, volume of goods, and weight of goods for each transportation task; S3. According to the integrated transportation model built in step S1 and the emergency material transportation tasks obtained in step S2, use the genetic algorithm to construct an alternative path set; considering the transportation tasks, vehicle quantity, and transportation capacity constraint conditions, use the tabu search algorithm to solve the emergency material transportation optimization scheduling problem, and select the optimal scheduling plan from the alternative path set.

2. The emergency material vehicle-cargo matching and loading optimization method based on two-stage hybrid tabu search according to claim 1, wherein In S1, the specific method for building the integrated transportation model for cargo loading and vehicle routing is as follows: The transportation network is represented by a directed graph G=(A, E), where the set of stations is A={a0, a1, …, a n}, where a0 is the parking lot, and a n is the nth station. The set of road segments is E={(a ij , …): i≠j}, and a ij represents the road segment between the ith station and the jth station. The parking lot has a transportation queue composed of m different types of transportation tools. The transportation tool k has a maximum load capacity G, a cost C, and a three-dimensional loading space with length L, width W, and height H. The volume of the loading space V = L * H * W. The cost of moving the transportation tool k=(1, …, m) from the ith station to the jth station is where d ij is the distance from station i to station j, c2 represents the transportation cost, m is the total number of transportation tools, n is the total number of stations, represents the decision variable of whether vehicle k travels from station i to station j. If vehicle k travels from station i to station j, the decision variable is 1, otherwise it is 0. y k represents the decision variable of whether vehicle k is used. If vehicle k is used, it is equal to 1, otherwise it is 0.

3. The emergency material vehicle-load matching and stowage optimization method based on two-stage hybrid tabu search according to claim 1, characterized in that S3 is specifically as follows: S3.

1. Take the set of emergency material transportation tasks within the planned period as the input. Each task includes the starting location, destination location, quantity of goods, volume, and weight; S3.

2. Initialize the genetic algorithm to obtain a population. This population contains multiple individuals, and each individual is a transportation and loading plan; S3.

3. Construct a fitness function based on the total transportation time and the loading rate of distribution vehicles; S3.

4. Initialize the global optimal solution S best and the current solution S t , and set the maximum number of iterations; S3.

5. Perform a crossover operation on the selected individuals using the two-point crossover method to obtain offspring individuals, and then perform a mutation operation on the selected individuals to obtain offspring individuals as the alternative path set; S3.

6. Use the tabu search algorithm to optimize the offspring individuals; S3.

7. Select a pair of destruction operators and repair operators to perform neighborhood search operations to generate a new solution S new ; S3.

8. Determine whether the new solution meets the aspiration criterion. If it meets, use the solution that meets the aspiration criterion as the current solution, and use the plan corresponding to the current solution to replace the object that entered the tabu list earliest, update the optimal state, and re-execute step S3.6; otherwise, enter step S3.9; S3.

9. Determine the tabu attribute of the new solution, and use the best solution corresponding to the non-tabu object as the current solution, and use the plan corresponding to the current solution to replace the object that entered the tabu list earliest; S3.

10. Compare the new solution S new with the current solution S t in terms of the fitness function value. If the new solution is better than the current solution, directly replace the current solution with the new solution. At the same time, update the global optimal solution S best ; Determine whether the maximum number of iterations has been reached. If so, output the global optimal solution S best as the optimal scheduling plan. Otherwise, return to step S3.

7.

4. The emergency material vehicle-cargo matching and stowage optimization method based on two-stage hybrid tabu search according to claim 3, characterized in that In S3.3, the specific fitness function is as follows: Among them, C represents the total scheduling cost, c1 represents the loading cost, c2 represents the transportation cost, where d ij is the distance from station i to station j, m is the total number of transportation tools, n is the total number of stations, is the decision variable indicating whether vehicle k travels from station i to station j. If vehicle k travels from station i to station j, the decision variable is 1, otherwise it is 0. y k is the decision variable indicating whether vehicle k is used. If vehicle k is used, it is equal to 1, otherwise it is 0; The constraint conditions of the fitness function include: Among them, Constraint (2) and Constraint (3) ensure that the loading route is a closed route. It represents the decision variable for vehicle k from the parking lot to station j. It represents the decision variable for vehicle k from station i to the parking lot. Constraint (4) restricts each station to be visited only once. Constraint (5) ensures that if a vehicle arrives at a station, then it must depart from this station to another station. h represents the intermediate station that the vehicle arrives at. Constraint (6) restricts that the items placed in the vehicle must be completely within the loading space. It represents the loading position of the items loaded in vehicle k in the x-axis direction of the vehicle. It represents the loading position of the items loaded in vehicle k in the y-axis direction of the vehicle. It represents the loading position of the items loaded in vehicle k in the z-axis direction of the vehicle. L is the maximum loading length in the x-axis direction of the vehicle, W is the maximum loading width in the y-axis direction of the vehicle, and Z is the maximum loading height in the z-axis direction of the vehicle.

5. The emergency material vehicle-load matching and stowage optimization method based on two-stage hybrid tabu search according to claim 3, characterized in that, In S3.7, the specific method for selecting a pair of destruction operators and repair operators is to randomly select a pair of destruction operators and repair operators from the following destruction operators and repair operators: Destruction operator 1: Randomly select 10% of the tasks and delete their solutions; Destruction operator 2: Randomly select 20% of the tasks and delete their solutions; Repair operator 1: For all deleted tasks, randomly reset the matching vehicles from the alternative solution set and randomly reset the transportation volume borne by the paths; Repair operator 2: For all deleted tasks, keep the original transportation vehicles and randomly reset the transportation volume borne by the paths.

6. An electronic device, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the emergency material vehicle-cargo matching and loading optimization method based on two-stage hybrid tabu search as described in any one of claims 1-5.

7. A computer-readable storage medium storing a computer program, characterized in that, The computer program causes the computer to execute the emergency material vehicle-cargo matching and loading optimization method based on two-stage hybrid tabu search as described in any one of claims 1-5.

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