Method and system for optimizing distribution vehicle path of assembly type prefabricated part

Abstract

The invention provides an assembly type prefabricated part distribution vehicle path optimization method and system, a storage medium and electronic equipment, and relates to the field of vehicle path optimization. According to the method, an objective function mathematical model is established for the vehicle distribution path optimization problem of the assembly type prefabricated parts, the model comprehensively considers the transportation cost and the loading and unloading cost, meanwhile, the actual constraint conditions in reality are considered, and the effectiveness and practicability of the model are ensured. Besides, based on problem characteristics, a genetic algorithm is improved, a two-dimensional array coding and roulette selection method is adopted, and row exchange crossover operation and random element adjustment mutation operation are introduced, so that the algorithm diversity is enhanced, and the probability of finding a global optimal solution is improved. Meanwhile, local search is carried out on the optimal individual of each generation in combination with a hill climbing algorithm, the search process is further refined, and the possibility of a better solution is explored.

Description

Technical Field

[0001] The present invention relates to the field of vehicle path optimization, and in particular to a method, system, storage medium and electronic device for optimizing the path of an assembled prefabricated component distribution vehicle. Background Art

[0002] The Vehicle Routing Problem (VRP) is a classic combinatorial optimization problem that involves how to effectively allocate a group of vehicles to visit multiple nodes and achieve the optimal goal while satisfying constraints.

[0003] In the related technology, patent CN202211636044.4 discloses a vehicle path optimization method based on genetic algorithm and sparrow algorithm, including: constructing a mathematical model of roads and transportation modes, initializing the mathematical model of roads and transportation modes of the genetic algorithm, and establishing an improved genetic algorithm incorporating the sparrow search algorithm; wherein, establishing the improved genetic algorithm incorporating the sparrow search algorithm includes: using the target population obtained by the genetic algorithm as the initial population; calculating the fitness value of the initial population, and classifying the initial population into explorers and followers according to the fitness value, and determining a preset proportion of the followers as vigilant; updating the explorer position, follower position and vigilant position respectively according to the sparrow search algorithm; bringing the sparrow search algorithm optimization results after the explorer position, follower position and vigilant position are updated into the genetic algorithm for iteration until the iteration ends to generate a path optimization method.

[0004] However, the above-mentioned existing technologies and their models mainly focus on vehicle path planning in the traditional logistics field, and are not directly optimized for the transportation scenarios of prefabricated components. Therefore, their adaptability and accuracy in this application scenario are subject to certain limitations. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a method, system, storage medium and electronic device for optimizing the routes of prefabricated component distribution vehicles, which solves the technical problem that the transportation scenario of prefabricated components is not directly optimized, and therefore the adaptability and accuracy in this application scenario are subject to certain limitations.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A method for optimizing the path of a prefabricated component distribution vehicle, comprising:

[0010] Obtain the demand for prefabricated components and vehicle resources;

[0011] Based on the demand for the prefabricated components and vehicle resources, a mathematical model is constructed with minimizing the total distribution cost as the optimization goal;

[0012] An improved genetic algorithm based on row exchange crossover, random element adjustment mutation and local search hill climbing algorithm is used to solve the mathematical model and obtain the optimal vehicle distribution path solution.

[0013] Preferably, the mathematical model includes:

[0014]

[0015]

[0016] Wherein, formula (1) is to minimize the total distribution cost, which includes transportation cost and loading and unloading cost; Min represents the minimization function, Z represents the total distribution cost; N represents the set of construction sites, N = {1, 2, ..., n}, where 1 is the distribution center; K represents the set of available vehicles, K = {1, 2, ..., k}; c ik represents the unit cost of a trip of vehicle k from distribution center 1 to construction site i; y ik is a decision variable, indicating the number of times vehicle k transports to construction site i, which is a non-negative integer; h i It represents the unit cost of transporting to construction site i for loading and unloading;

[0017] Formula (2) is the construction site demand to meet the constraint; W k represents the maximum load of vehicle k; Q ik W represents the maximum number of components transported by vehicle k to construction site i at one time; i It represents the total weight of goods required by construction site i on that day;

[0018] Formulas (3) to (4) are the constraints on variable values; x ik is a decision variable, which is 1 if vehicle k serves construction site i and transports at least one trip; otherwise, it is 0;

[0019] Formula (5) is the vehicle transport trip constraint; T k It represents the maximum number of transportations that vehicle k can make per day.

[0020] Preferably, the improved genetic algorithm based on row exchange crossover, random element adjustment mutation and local search hill climbing algorithm is used to solve the mathematical model and obtain the optimal vehicle distribution path solution; including:

[0021] S31, setting problem parameters and parameters of improved genetic algorithm, creating individual two-dimensional array coding template, and generating initial population according to constraint conditions, and calculating fitness value of each individual;

[0022] S32, starting an iterative process according to the improved genetic algorithm, and sequentially performing roulette wheel selection, row exchange crossover, and random element adjustment mutation operations;

[0023] S33, for the current optimal individual, use the local search hill climbing algorithm to operate and update the population;

[0024] S34, if the preset maximum number of iterations is reached, the optimal individual and its fitness value are output, and the process goes to S35; otherwise, the number of iterations is increased by one, and the process goes to S32;

[0025] S35. Output the optimal individual code, and decode to obtain the optimal vehicle delivery path solution.

[0026] Preferably, the creating of the individual two-dimensional array encoding template refers to:

[0027] Create a (n-1)·k-th element with y as its element. ik The two-dimensional integer array is used as the encoding, where the rows of the array represent construction sites and the columns represent vehicles.

[0028] Preferably, the fitness value of each individual is calculated, wherein the fitness function takes the inverse of the total delivery cost.

[0029] Preferably, the method of using a local search hill climbing algorithm to operate and update the population for the current optimal individual includes:

[0030] S331, selecting the best individual of the current generation as the starting point of the hill climbing algorithm;

[0031] S332: Generate a new solution in the neighborhood of the individual, specifically by taking the following operations:

[0032] Randomly select vehicle k and construction site i, and check whether the demand of construction site i can still be met by other vehicles after reducing the number of transportation times of vehicle k to construction site i; if so, perform the deletion operation, denoted as y ik =y ik -1, generate a new solution; if not, abandon this operation and make a new random selection;

[0033] Repeat the above random selection and evaluation process until a valid new solution is generated or the preset maximum number of attempts is reached; if a valid new solution cannot be generated after the maximum number of attempts, the initial solution is used as the new solution and the search is completed once;

[0034] S333. Calculate the fitness value of the new solution and compare it with the fitness value of the current solution.

[0035] S334. If the fitness value of the new solution is higher, the new solution is accepted and used to replace the current solution.

[0036] S335. Repeat steps S332 to S334 until the maximum number of searches is reached.

[0037] A system for optimizing the path of a prefabricated component distribution vehicle, comprising:

[0038] An acquisition module is used to obtain the demand for prefabricated components and vehicle resources;

[0039] A construction module is used to construct a mathematical model based on the demand for the prefabricated components and vehicle resources with the goal of minimizing the total distribution cost;

[0040] The solution module is used to solve the mathematical model and obtain the optimal vehicle distribution path solution by adopting an improved genetic algorithm based on row exchange crossover, random element adjustment mutation and local search hill climbing algorithm.

[0041] A storage medium stores a computer program for optimizing the route of a distribution vehicle for assembled prefabricated components, wherein the computer program enables a computer to execute the method for optimizing the route of a distribution vehicle for assembled prefabricated components as described above.

[0042] An electronic device, comprising:

[0043] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the above-mentioned method for optimizing the route of the assembled prefabricated component delivery vehicle.

[0044] (III) Beneficial effects

[0045] The present invention provides a method, system, storage medium and electronic device for optimizing the path of a prefabricated component distribution vehicle. Compared with the prior art, the method has the following beneficial effects:

[0046] The present invention can optimize the vehicle delivery path and reduce the total delivery cost of prefabricated components. The algorithm design combines global and local search capabilities, improves the search efficiency and stability of the algorithm, and thus finds a better delivery path. At the same time, it can also bring certain cost savings and efficiency improvements to the prefabricated component industry, and provide support for solving the delivery path optimization problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A block diagram of a method for optimizing the path of a prefabricated component distribution vehicle provided by an embodiment of the present invention;

[0049] Figure 2 A flowchart of a method for optimizing the route of a prefabricated component distribution vehicle provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The embodiments of the present application provide a method, system, storage medium and electronic device for optimizing the route of distribution vehicles for prefabricated components, thereby solving the technical problem that the transportation scenario of prefabricated components is not directly optimized and therefore the adaptability and accuracy in this application scenario are subject to certain limitations.

[0052] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0053] The embodiment of the present invention first constructs a mathematical model for optimizing the path of prefabricated component distribution vehicles and sets constraints; secondly, performs two-dimensional array encoding, initializes the population and designs a fitness function; then designs an improved genetic algorithm based on row exchange crossover, random element adjustment mutation and local search hill climbing algorithm; finally, executes the algorithm and outputs the historically optimal solution as the final solution, which is the optimal vehicle distribution path plan after decoding.

[0054] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0055] Embodiment 1:

[0056] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing the path of a prefabricated component distribution vehicle, comprising:

[0057] S1. Obtain the demand for prefabricated components and vehicle resources;

[0058] S2. Based on the demand for the prefabricated components and vehicle resources, a mathematical model is constructed with minimizing the total distribution cost as the optimization goal;

[0059] S3. Using an improved genetic algorithm based on row exchange crossover, random element adjustment mutation and local search hill climbing algorithm to solve the mathematical model and obtain the optimal vehicle distribution path solution.

[0060] The embodiments of the present invention can optimize the vehicle delivery path and reduce the total delivery cost of prefabricated components. The algorithm design combines global and local search capabilities, improves the search efficiency and stability of the algorithm, and thus finds a better delivery path. At the same time, it can also bring certain cost savings and efficiency improvements to the prefabricated component industry, and provide support for solving the delivery path optimization problem.

[0061] like Figure 2 As shown, Figure 2 A flowchart of the method for optimizing the vehicle routing for prefabricated component distribution is given. Figure 2 The following are the steps of the above solution:

[0062] In step S1, the demand for prefabricated components and vehicle resources are obtained.

[0063] In this step, the demand for prefabricated components and vehicle resources required for the subsequent mathematical model construction are obtained, and the symbols are shown in Table 1:

[0064] Table 1

[0065]

[0066] In step S2, based on the demand for the prefabricated components and vehicle resources, a mathematical model is constructed with minimizing the total distribution cost as the optimization goal.

[0067] It is necessary to first explain the assumptions of the embodiments of the present invention:

[0068] (1) The distribution center has enough vehicles to ensure that the component needs of all construction sites can be met on the same day.

[0069] (2) The vehicles at the distribution center may have different carrying capacities, but each vehicle has the ability to transport all types of components required to the construction site.

[0070] (3) After completing the delivery task at a construction site, each delivery vehicle must return directly to the distribution center instead of continuing to the next construction site.

[0071] Specifically, the mathematical model constructed in this step includes:

[0072]

[0073] Wherein, formula (1) is to minimize the total distribution cost, which includes transportation cost and loading and unloading cost; Min represents the minimization function, Z represents the total distribution cost; N represents the set of construction sites, N = {1, 2, ..., n}, where 1 is the distribution center; K represents the set of available vehicles, K = {1, 2, ..., k}; c ik represents the unit cost of a trip of vehicle k from distribution center 1 to construction site i; y ik is a decision variable, indicating the number of times vehicle k transports to construction site i, which is a non-negative integer; h i It represents the unit cost of transporting to construction site i for loading and unloading;

[0074] Formula (2) is the construction site demand to meet the constraint; W k represents the maximum load of vehicle k; Q ik W represents the maximum number of components transported by vehicle k to construction site i at one time; i It represents the total weight of goods required by construction site i on that day;

[0075] Formulas (3) to (4) are the constraints on variable values; x ik is a decision variable, which is 1 if vehicle k serves construction site i and transports at least one trip; otherwise, it is 0;

[0076] Formula (5) is the vehicle transport trip constraint; T k It represents the maximum number of transportations that vehicle k can make per day.

[0077] In step S3, an improved genetic algorithm based on row exchange crossover, random element adjustment mutation and local search hill climbing algorithm is used to solve the mathematical model and obtain the optimal vehicle delivery path solution.

[0078] It should be noted that the traditional genetic algorithm has a strong global search capability and can find potential high-quality solutions in a large solution space, but its local search capability is relatively weak. The hill climbing algorithm is an efficient local search algorithm that can quickly approach the local optimal solution. However, it is easy to fall into the local optimal solution and cannot jump out, resulting in the inability to find the global optimal solution.

[0079] In particular, the embodiment of the present invention combines the above two algorithms, fully utilizing the global search capability of the genetic algorithm to prevent the hill climbing algorithm from falling into the local optimum, and at the same time utilizing the local search capability of the hill climbing algorithm to accelerate the convergence speed of the genetic algorithm in the area close to the optimal solution.

[0080] Specifically, this step includes:

[0081] S31. Set the problem parameters and the parameters of the improved genetic algorithm, create an individual two-dimensional array coding template, generate an initial population according to the constraints, and calculate the fitness value of each individual.

[0082] The creation of an individual two-dimensional array encoding template refers to:

[0083] Create a (n-1)·k-th element with y as its element. ik The two-dimensional integer array is used as the encoding, where the rows of the array represent construction sites and the columns represent vehicles.

[0084] Correspondingly, generating the initial population according to the constraint conditions means:

[0085] Determine the population size, that is, the number of individuals to be generated. For each individual, generate a (n-1)×k two-dimensional integer array as the encoding, where each element y ik Randomly assign non-negative integer values ​​that satisfy the total weight and total volume constraints of the goods required at construction site i. Check whether each generated individual satisfies all constraints. If not, regenerate until they are satisfied. Repeat this process until a sufficient number of individuals are generated to form the initial population.

[0086] In addition, the fitness value of each individual is calculated, where the fitness function takes the inverse of the total delivery cost, that is,

[0087] S32, starting an iterative process according to the improved genetic algorithm, and sequentially executing roulette wheel selection, row exchange crossover, and random element adjustment mutation operations.

[0088] Specifically, the roulette wheel selection, row exchange crossover, and random element adjustment mutation operations are as follows:

[0089] (1) Selection operation: Use the roulette wheel selection method to select excellent individuals to enter the next generation based on the individual's fitness value. The specific steps include:

[0090] First, the sum of the fitness values ​​of all individuals in the population is calculated, and then a selection probability proportional to its fitness value is assigned to each individual. Next, a random number is generated, and individuals are selected to enter the next generation based on this selection probability. At the same time, in order to ensure that excellent genes are not lost, an elite retention strategy is adopted to retain the best individuals in the current generation.

[0091] (2) Crossover operation: Design a crossover operation for a two-dimensional array to explore new solution spaces. Specifically, the row exchange crossover method is used:

[0092] First, two parent individuals are randomly selected as crossover objects. Then, one or several rows of the two parent individuals are exchanged to generate two new offspring individuals. Finally, check whether the newly generated offspring individuals meet all the constraints. If not, repair or re-cross to ensure that the generated offspring individuals are valid.

[0093] (3) Mutation operation: mutate the two-dimensional array to increase the diversity of the population and explore new solutions. The specific mutation method is to adjust the random elements:

[0094] First, two random numbers are used to determine the row and column of the individual to be mutated, thereby selecting an element in the individual to be mutated; then a small adjustment is made within the feasible domain of the element, that is, an integer 1 is added or subtracted from the current value of the element, but it is ensured that the adjusted value does not violate the constraints of the maximum number of transportation times and at least one transportation of the vehicle. Finally, check whether the adjusted individual meets all constraints. If not, repair or re-mutate.

[0095] S33, for the current best individual, use the local search hill climbing algorithm to operate and update the population; the specific steps are as follows:

[0096] S331, selecting the best individual of the current generation as the starting point of the hill climbing algorithm;

[0097] S332: Generate a new solution in the neighborhood of the individual, specifically by taking the following operations:

[0098] Randomly select vehicle k and construction site i, and check whether the demand of construction site i can still be met by other vehicles after reducing the number of transportation times of vehicle k to construction site i; if so, perform the deletion operation, denoted as y ik =y ik -1, generate a new solution; if not, abandon this operation and make a new random selection;

[0099] Repeat the above random selection and evaluation process until a valid new solution is generated or the preset maximum number of attempts is reached; if a valid new solution cannot be generated after the maximum number of attempts, the initial solution is used as the new solution and the search is completed once;

[0100] S333. Calculate the fitness value of the new solution and compare it with the fitness value of the current solution.

[0101] S334. If the fitness value of the new solution is higher, the new solution is accepted and used to replace the current solution.

[0102] S335. Repeat steps S332 to S334 until the maximum number of searches is reached.

[0103] S34, if the preset maximum number of iterations is reached, the optimal individual and its fitness value are output, and the process goes to S35; otherwise, the number of iterations is increased by one, and the process goes to S32;

[0104] S35. Output the optimal individual code, and decode to obtain the optimal vehicle delivery path solution.

[0105] So far, the embodiment of the present invention has completed the entire process of the method for optimizing the route of prefabricated component delivery vehicles.

[0106] Embodiment 2:

[0107] An embodiment of the present invention provides a system for optimizing the path of a prefabricated component distribution vehicle, comprising:

[0108] An acquisition module is used to obtain the demand for prefabricated components and vehicle resources;

[0109] A construction module is used to construct a mathematical model based on the demand for the prefabricated components and vehicle resources with the goal of minimizing the total distribution cost;

[0110] The solution module is used to solve the mathematical model and obtain the optimal vehicle distribution path solution by adopting an improved genetic algorithm based on row exchange crossover, random element adjustment mutation and local search hill climbing algorithm.

[0111] Embodiment 3:

[0112] An embodiment of the present invention provides a storage medium storing a computer program for optimizing the route of a prefabricated component distribution vehicle, wherein the computer program enables a computer to execute the method for optimizing the route of a prefabricated component distribution vehicle as described in Example 1.

[0113] Embodiment 4:

[0114] An embodiment of the present invention provides an electronic device, including:

[0115] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the method for optimizing the route of a prefabricated component distribution vehicle as described in Example 1.

[0116] It is understandable that the system, storage medium and electronic device for optimizing the path of prefabricated component delivery vehicles provided in the embodiments of the present invention correspond to the method for optimizing the path of prefabricated component delivery vehicles provided in the embodiments of the present invention, and the explanations, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the method for optimizing the path of prefabricated component delivery vehicles, which will not be repeated here.

[0117] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0118] 1. The embodiment of the present invention aims at the problem of optimizing the distribution path of prefabricated components by vehicles and establishes a mathematical model of objective function. The model comprehensively considers the transportation cost and the loading and unloading cost, and also considers the actual constraints in reality, thus ensuring the validity and practicability of the model.

[0119] 2. Based on the characteristics of the problem, the embodiment of the present invention improves the genetic algorithm, adopts two-dimensional array encoding and roulette selection method, and introduces row exchange crossover operation and random element adjustment mutation operation to enhance algorithm diversity and improve the probability of finding the global optimal solution. At the same time, the best individual of each generation is searched locally in combination with the hill climbing algorithm to further refine the search process and explore the possibility of a better solution.

[0120] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0121] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the path of a prefabricated component distribution vehicle, characterized in that: include: Obtain the demand for prefabricated components and vehicle resources; Based on the demand for the prefabricated components and vehicle resources, a mathematical model is constructed with minimizing the total distribution cost as the optimization goal; An improved genetic algorithm based on row exchange crossover, random element adjustment mutation and local search hill climbing algorithm is used to solve the mathematical model and obtain the optimal vehicle distribution path solution.

2. The method for optimizing the route of prefabricated component distribution vehicles according to claim 1, characterized in that: The mathematical model includes: Wherein, formula (1) is to minimize the total distribution cost, which includes transportation cost and loading and unloading cost; Min represents the minimization function, Z represents the total distribution cost; N represents the set of construction sites, N = {1, 2, ..., n}, where 1 is the distribution center; K represents the set of available vehicles, K = {1, 2, ..., k}; c ik represents the unit cost of a trip of vehicle k from distribution center 1 to construction site i; y ik is a decision variable, indicating the number of times vehicle k transports to construction site i, which is a non-negative integer; h i It represents the unit cost of transporting to construction site i for loading and unloading; Formula (2) is the construction site demand to meet the constraint; W k represents the maximum load of vehicle k; Q ik W represents the maximum number of components transported by vehicle k to construction site i at one time; i It represents the total weight of goods required by construction site i on that day; Formulas (3) to (4) are the constraints on variable values; x ik is a decision variable, which is 1 if vehicle k serves construction site i and transports at least one trip; otherwise, it is 0; Formula (5) is the vehicle transport trip constraint; T k It represents the maximum number of transportations that vehicle k can make per day.

3. The method for optimizing the route of prefabricated component distribution vehicles according to claim 2, characterized in that: The improved genetic algorithm based on row exchange crossover, random element adjustment mutation and local search hill climbing algorithm is used to solve the mathematical model and obtain the optimal vehicle distribution path solution; include: S31, setting problem parameters and parameters of improved genetic algorithm, creating individual two-dimensional array coding template, and generating initial population according to constraint conditions, and calculating fitness value of each individual; S32, starting an iterative process according to the improved genetic algorithm, and sequentially performing roulette wheel selection, row exchange crossover, and random element adjustment mutation operations; S33, for the current optimal individual, use the local search hill climbing algorithm to operate and update the population; S34, if the preset maximum number of iterations is reached, the optimal individual and its fitness value are output, and the process goes to S35; otherwise, the number of iterations is increased by one, and the process goes to S32; S35. Output the optimal individual code, and decode to obtain the optimal vehicle delivery path solution.

4. The method for optimizing the route of prefabricated component distribution vehicles according to claim 3, characterized in that: The creation of an individual two-dimensional array encoding template refers to: Create a (n-1)·k-th order array with yi as its element. k The two-dimensional integer array of is used as the encoding, where the rows of the array represent construction sites and the columns represent vehicles.

5. The method for optimizing the route of prefabricated component distribution vehicles according to claim 3, characterized in that: The fitness value of each individual is calculated, wherein the fitness function takes the inverse of the total delivery cost.

6. The method for optimizing the route of prefabricated component distribution vehicles according to claim 3, characterized in that: The method adopts a local search and hill climbing algorithm to operate and update the population for the current optimal individual; including: S331, selecting the best individual of the current generation as the starting point of the hill climbing algorithm; S332: Generate a new solution in the neighborhood of the individual, specifically by taking the following operations: Randomly select vehicle k and construction site i, and check whether the demand of construction site i can still be met by other vehicles after reducing the number of transportation times of vehicle k to construction site i; if so, perform the deletion operation, represented by y ik =y ik -1, generate a new solution; if not, abandon this operation and make a new random selection; Repeat the above random selection and evaluation process until a valid new solution is generated or the preset maximum number of attempts is reached; if a valid new solution cannot be generated after the maximum number of attempts, the initial solution is used as the new solution and the search is completed once; S333. Calculate the fitness value of the new solution and compare it with the fitness value of the current solution. S334. If the fitness value of the new solution is higher, the new solution is accepted and used to replace the current solution. S335. Repeat steps S332 to S334 until the maximum number of searches is reached.

7. A system for optimizing the path of prefabricated component distribution vehicles, characterized in that: include: An acquisition module is used to obtain the demand for prefabricated components and vehicle resources; A construction module is used to construct a mathematical model based on the demand for the prefabricated components and vehicle resources with the goal of minimizing the total distribution cost; The solution module is used to solve the mathematical model and obtain the optimal vehicle distribution path solution by adopting an improved genetic algorithm based on row exchange crossover, random element adjustment mutation and local search hill climbing algorithm.

8. A storage medium, characterized in that: The computer program for optimizing the route of a distribution vehicle for assembled prefabricated components is stored therein, wherein the computer program enables the computer to execute the method for optimizing the route of a distribution vehicle for assembled prefabricated components as claimed in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: one or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the programs comprising a method for executing the method for optimizing the route of a prefabricated component distribution vehicle as described in any one of claims 1 to 6.

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