Different-vehicle-type multi-cabin vehicle path planning method and device based on multi-strategy iteration local search

Abstract

The invention relates to the technical field of vehicle path planning, in particular to a different-vehicle-type multi-cabin vehicle path planning method and device based on multi-strategy iteration local search. The method comprises the following steps: acquiring vehicle information and node information, and establishing a path cost minimization relational expression; obtaining an initial solution Csol according to the vehicle information and the node information by using a cheapest insertion method; performing lifting optimization on the initial solution Csol through a loading capacity adjustment strategy LAS, and outputting a lifting solution a; adjusting the lifting solution a through a local search strategy to obtain a best solution Bsol; based on the best solution Bsol, adjusting through fixed random number disturbance to obtain a new best solution; based on the best solution Bsol and the new best solution, calculating a path loading capacity percentage variance and a path cost, and judging whether a preset acceptance strategy is met or not; judging whether an iteration stopping condition is met or not; and optimizing the new best solution Bsol through a vehicle model optimization strategy VOS to obtain a global optimal solution Asol.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle path planning, and in particular to a method and device for path planning of vehicles with different vehicle types and multiple cabins based on multi-strategy iterative local search. Background Art

[0002] In recent years, with the continuous development of China's logistics and the increasing complexity of modern supply chains, people's requirements for the diversity of transported goods and even the entire transportation industry are constantly increasing. The use of traditional single transport vehicles has gradually failed to meet the growing and diversified needs of various industries. If ordinary single-cabin vehicles are still used for distribution during transportation, only one item can be transported at a time, which seriously affects the efficiency and cost of transportation. Therefore, if multi-cabin vehicles of various models can be widely used in the transportation industry, the flexibility and efficiency of the transportation process can be significantly improved, the risks brought by the mixing of different commodities can be reduced, the transportation safety can be improved, and the high transportation costs can be optimized.

[0003] The Multi-Compartment Vehicle Routing Problem (MCVRP) is an NP-hard problem and a variant of the Vehicle Routing Problem (VRP). Different from the traditional VRP problem where each vehicle can only provide delivery services for one commodity, in the MCVRP problem, each vehicle has multiple independent compartments and can deliver multiple mutually exclusive commodities at the same time to achieve the goals of efficiency, timeliness, and economy. The Multi-Compartment Vehicle Routing Problem with Different Vehicle Types is based on the MCVRP problem and adds the problem attribute of different vehicle types to combine the two for research. For the Multi-Compartment Vehicle Routing Problem with Different Vehicle Types, traditional solutions often have certain limitations: for example, although the exact algorithm has a good performance in solving small-scale problems. However, the MCVRP problem is an NP-hard problem. After introducing the attribute of multiple vehicle types, the problem solution will be more complicated. Therefore, when facing large-scale problems, using the exact algorithm to solve the problem is not only ineffective, but also has no guarantee of solution time. Therefore, it is particularly important to expand the research attributes of the Multi-Compartment Vehicle Routing Problem with Different Vehicle Types, build a problem model, and design efficient and high-quality algorithms.

[0004] The iterative local search algorithm is a meta-heuristic optimization algorithm that aims to find a better solution by applying local search multiple times and jumping out of the local optimal solution. The iterative local search algorithm can not only avoid falling into the local optimal solution through perturbations, but also solve the problem more efficiently. Combined with flexible multi-strategy adjustment and wide applicability, the iterative local search algorithm has certain advantages when facing multi-model and multi-cabin path planning problems. Summary of the invention

[0005] Aiming at the problem that existing algorithms have difficulty in generating initial solutions and poor solution quality when solving the path problem of vehicles with different models and multiple cabins, the present invention proposes a path planning method and device for vehicles with different models and multiple cabins based on multi-strategy iterative local search.

[0006] In one aspect, the present invention provides a method for path planning of vehicles with different vehicle types and multiple cabins based on multi-strategy iterative local search, comprising:

[0007] Step 1: Obtain vehicle information and node information, and establish a path cost minimization equation; the node information includes customer nodes and station nodes; the vehicle information includes the vehicle model information and the total number of all vehicle models;

[0008] Step 2: According to the vehicle information and node information, the cheapest insertion method is used to obtain the initial solution C_sol of the path; the cheapest insertion method is to first generate the first part of the path according to the customer nodes with large demand, remove the part of nodes in the node information, and then randomly select some customer nodes from the remaining node information to generate the second part of the path; the first part of the path and the second part of the path constitute the initial path, and the other remaining customer nodes are inserted into the initial path to obtain the initial solution; the number of the initial paths is equal to the total number of all vehicle models;

[0009] Step 3: The initial solution C_sol is improved and optimized by the load adjustment strategy LAS, and the improved solution a is output; the load adjustment strategy LAS is used to improve the loading rate of smaller vehicles on the path and optimize the loading space of large vehicles;

[0010] Step 4: Adjust the improved solution a through the local search strategy to obtain the best solution B_sol;

[0011] Step 5: Based on the best solution B_sol, adjust it by fixed random number perturbation to get a new best solution;

[0012] Step 6: Based on the best solution B_sol and the new best solution, calculate the path load percentage variance and path cost. If the path load percentage variance and path cost meet the preset acceptance strategy, accept the new best solution and execute step 7; otherwise, return to step 4 and repeat;

[0013] Step 7: Determine whether the iteration stop condition is met. If the iteration stop condition is met, execute step 8, otherwise return to step 4 and repeat;

[0014] Step 8: Optimize the new best solution through the vehicle type optimization strategy VOS to obtain the global optimal solution A_sol; the vehicle type optimization strategy VOS is used to optimize the relationship between the path loading capacity in the new best solution and the maximum capacity of the corresponding vehicle type.

[0015] Furthermore, in step 1,

[0016] The path cost minimization relationship is:

[0017]

[0018] Where F represents the path cost of the corresponding solution; V represents the node information; M represents the set of used vehicle models, M = {1, 2, ..., m}; x ijm represents a binary decision variable, x ijm =1 means there is a vehicle of model m going from node i to node j, otherwise x ijm =0;d ij represents the path distance from node i to node j; c m Represents the unit transportation cost of vehicle type m.

[0019] Furthermore, step 1 also includes presetting required parameters, and the parameters required for initialization include setting a maximum number of iterations T, a neighborhood size NR, a perturbation parameter S, a perturbation number NP, and a number C of no-improvement acceptance strategies.

[0020] Further, in step 2, the use of the cheapest insertion method to obtain the path initial solution C_sol specifically includes:

[0021] Count the total number of all models in the model set N, and select from the node information set V in turn according to the demand size client nodes, generating Paths are the first part of the paths, and these customer nodes are removed from the node information set V; randomly select the remaining customer nodes from the node information set V client nodes, generating The paths are the second part of the paths, and these customer nodes are removed from the node information set V; a total of N initial paths are generated, and the vehicle types with maximum loading capacity exceeding the current demand are assigned to the initial paths; based on the N initial paths, under the constraint of meeting the maximum capacity, the remaining customer nodes in the node information set V are inserted into the vehicles that minimize the path cost; if there are customer nodes that cannot be inserted into the initial path, the solution is abandoned and the initial solution is regenerated.

[0022] Furthermore, in step 3, the load adjustment strategy LAS specifically includes:

[0023] Get the set of all vehicle models used in the current solution M = {1, 2, ..., m}, traverse all non-minimum vehicle models in order from large to small, and get the set of all paths using vehicle model r Fr = {R1, R2, ..., R i}, where r∈M and r≠1; set the current vehicle model to r=m;

[0024] For each path Ri in Fr, traverse the customer nodes on its path, and insert the customer nodes into the path using a smaller vehicle type t, where t < r, without violating the capacity limit and without increasing the total path cost;

[0025] After all paths in Fr have been traversed, traverse the next vehicle type r - 1; determine whether r - 1 is greater than 1. If so, continue to traverse vehicle type r - 1; otherwise, end the adjustment; set the iteration count T to 1 and the non - improvement count C to 0.

[0026] Further, in step 5, the fixed random number perturbation specifically includes:

[0027] Each time of perturbation, randomly select one path from N paths, randomly select a customer node in the selected path as a reference, remove b customer nodes within its neighborhood range NR, and temporarily store them in an array;

[0028] Insert the customer nodes temporarily stored in the array into the path in a random order;

[0029] Determine whether the path cost of the new solution obtained after the current perturbation is less than that of the best solution before the current perturbation. If it is less, use the new solution as the new best solution for the next perturbation; otherwise, use the best solution before the current perturbation for the next perturbation;

[0030] Until the perturbation count NP reaches the preset count, end the perturbation and output the finally obtained new best solution.

[0031] Further, in step 6, the calculation expression for the variance of the path loading percentage is:

[0032]

[0033] where n represents the number of paths corresponding to the solution x, n ≤ N; P i represents the loading rate of the vehicle corresponding to path R i The loading rate is the ratio of the load on the vehicle corresponding to path R i to the maximum capacity of the vehicle type, and Z represents the average loading rate of all vehicles on all paths P1, P2... P n

[0034] Further, in step 6, the preset acceptance strategy is defined as:

[0035]

[0036] where S c represents the best solution B_sol, S n represents the new best solution, F(x) represents the path cost, and f(x) represents the variance of the path loading percentage; ​

[0037] If the new best solution has a smaller path cost, then accept the new best solution, increase the number of iterations T by 1, and return the number of times the strategy is not improved C to 0; if the path cost of the new best solution remains unchanged or increases, but the variance of the percentage of path load decreases, then accept the new best solution, increase the number of iterations T by 1, and return the number of times the strategy is not improved C to 0; in other cases, do not accept the new best solution, and increase the number of times the strategy is not improved C by 1;

[0038] Correspondingly, in step 7, the iteration stop condition includes determining whether the number of iterations T and the number of no-improvement times C have both reached preset values, and if so, executing step 8; otherwise, returning to step 4 and repeating.

[0039] Furthermore, in step 8, the vehicle type optimization strategy VOS specifically includes:

[0040] Get the model set M = {1, 2, ..., m} used by the current new best solution, and traverse from model 1 to model m in ascending order; set the current model to i, and get the path set f using model i i = {R1, R2, ..., R i} and the set of all paths whose load does not exceed the maximum capacity of vehicle type i I = {R1, R2, ..., R I};

[0041] When the number of paths corresponding to vehicle type i is less than the number of paths whose load does not exceed the maximum capacity of vehicle type i, for set F I Path R1 to Path R I Sort in descending order according to the path length, and then assign model i to this part of the path in order; after completing the assignment of model i, it is necessary to move on to the next model i+1; determine whether i is less than or equal to m, if so, continue to traverse the next model i+1, otherwise end the adjustment.

[0042] On the other hand, the present invention provides a vehicle path planning device for different vehicle types and multiple cabins based on multi-strategy iterative local search, comprising:

[0043] Data acquisition module: used to acquire vehicle information and node information, and establish a path cost minimization equation. The node information includes customer nodes and station nodes; the vehicle information includes the vehicle model information and the total number of all vehicle models;

[0044] Initial result module: used to obtain the initial path solution C_sol using the cheapest insertion method according to the vehicle information and node information; the cheapest insertion method refers to first generating the first part of the path according to the customer nodes with large demand, removing the part of nodes from the node information, and then randomly selecting some customer nodes from the remaining node information to generate the second part of the path; the first part of the path and the second part of the path constitute the initial path, and the remaining customer nodes are inserted into the initial path to obtain the initial solution; the number of the initial paths is equal to the total number of all vehicle models;

[0045] Loading adjustment module: used to improve and optimize the initial solution C_sol through the loading adjustment strategy LAS, and output the improved solution a; the loading adjustment strategy LAS is used to improve the loading rate of smaller vehicles on the path and optimize the loading space of large vehicles;

[0046] Local search module: used to adjust the improved solution a through the local search strategy to obtain the best solution B_sol;

[0047] Perturbation module: used to adjust the best solution B_sol through fixed random number perturbation to obtain a new best solution;

[0048] The first judgment module is used to calculate the path load percentage variance and path cost based on the best solution B_sol and the new best solution. If the path load percentage variance and path cost meet the preset acceptance strategy, the new best solution is accepted and the second judgment module is entered; otherwise, the local search module is returned to repeat the operation;

[0049] The second judgment module is used to judge whether the iteration stop condition is met; if the iteration stop condition is met, the vehicle model optimization module is entered, otherwise, the local search module is returned to repeat the operation;

[0050] Vehicle type optimization module: used to optimize the new best solution through the vehicle type optimization strategy VOS to obtain the global optimal solution A_sol; the vehicle type optimization strategy VOS is used to optimize the relationship between the path loading capacity in the new best solution and the maximum capacity of the corresponding vehicle type.

[0051] Compared with the existing technology, the present invention has the following beneficial effects:

[0052] (1) The method for path planning of vehicles with different models and multiple cabins based on multi-strategy iterative local search provided by the present invention constructs an initial solution by using an initial solution construction method based on the cheapest insertion method. This paper ensures the feasibility of generating a solution by giving priority to a certain number of customer points with high demand. At the same time, when inserting a node, the location with the lowest insertion cost is selected to further ensure the quality of the solution, thereby solving the problem of difficulty in generating an initial solution and low quality.

[0053] (2) In the process of adjusting the initial solution, the present invention designs a variety of strategies. Among them, the load adjustment strategy is designed to adjust the load of different vehicle types between paths, and optimizes a larger space for large vehicle types by increasing the load of small vehicle types, with the purpose of providing more optimization possibilities for local search. In order to enhance the stability of the solution and to avoid the solution from falling into the local optimum, the present invention designs a path load percentage variance acceptance strategy, which tends to accept solutions with better results and more stable loading rates between vehicles according to different acceptance criteria.

[0054] (3) The present invention uses a vehicle type optimization strategy to allocate vehicle types in descending order of path length, so that relatively long paths are allocated vehicle types with lower path costs. This strategy is used to optimize the best solution finally obtained to ensure that the vehicle type allocation is the optimal solution under the current circumstances, thereby improving the quality of vehicle type allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic flow chart of a method for path planning of vehicles of different vehicle types and multiple cabins based on multi-strategy iterative local search is provided for the implementation of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all 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.

[0057] like Figure 1 As shown, an embodiment of the present invention provides a path planning method for vehicles of different vehicle types and multiple cabins based on multi-strategy iterative local search, comprising:

[0058] S101: Acquire vehicle information and node information, and establish a path cost minimization equation; the node information includes customer nodes and station nodes; the vehicle information includes information on the vehicle model used and the total number of all vehicle models.

[0059] Specifically, it also includes preset required parameters, and the parameters required for initialization include setting the maximum number of iterations T, the neighborhood size NR, the disturbance parameter S, the number of disturbances NP, and the number of times C when the acceptance strategy is not improved.

[0060] Specifically, the path cost minimization relationship is:

[0061]

[0062] Where F represents the path cost of the corresponding solution; V represents the node information; M represents the set of used vehicle models, M = {1, 2, ..., m}; x ijm represents a binary decision variable, x ijm =1 means there is a vehicle of model m going from node i to node j, otherwise x ijm =0;d ij represents the path distance from node i to node j; c m Represents the unit transportation cost of vehicle type m.

[0063] S102: According to the vehicle information and the node information, the cheapest insertion method is used to obtain the initial solution C_sol of the path; the cheapest insertion method refers to first generating the first part of the path according to the customer nodes with larger demand, removing the part of the nodes from the node information, and then randomly selecting some customer nodes from the remaining node information to generate the second part of the path; the first part of the path and the second part of the path constitute the initial path, and the other remaining customer nodes are inserted into the initial path to obtain the initial solution; the number of the initial paths is equal to the total number of all vehicle models.

[0064] Specifically, count the total number of all models in the model set N, and select from the set V in order according to the demand size. The customer nodes with the largest demand are inserted into the path to generate 0-i-0 paths, and then randomly select from V Customer nodes are inserted into the path, generating 0—i—0 paths. After a total of N paths are generated, these N nodes are removed from V, and the vehicle models with maximum loads exceeding the current demand are assigned to these paths. Finally, the remaining customer nodes in the set V are sorted in descending order according to the demand size, and the remaining nodes are inserted into the vehicle with the lowest path cost without violating the maximum capacity constraint. The nodes are removed from V while the paths are updated. If there is a node that cannot be inserted, it means that a feasible solution has not been generated. The generated path solution is abandoned, and the step is re-executed to generate a new path until all customer nodes are inserted into the N paths initially generated.

[0065] It can be seen that the embodiment of the present invention generates an initial solution by the cheapest insertion method, solving the problem of difficulty in generating an initial solution and low quality of the vehicle routing problem with multiple compartments of different vehicle types. The cheapest insertion method can effectively avoid generating the same initial solution in different experiments, ensuring the diversity of the initial solution.

[0066] S103: The initial solution C_sol is improved and optimized by the load adjustment strategy LAS, and the improved solution a is output; the load adjustment strategy LAS is used to improve the loading rate of smaller vehicles on the path and optimize the loading space of larger vehicles;

[0067] Specifically, obtain the set of all vehicle types M = {1, 2, …, m} used for the current solution, and traverse all non-smallest vehicle types in descending order; set the current vehicle type as r = m; when traversing any vehicle type r, obtain the set of all paths Fr = {R1, R2, …, R i}, where r ∈ M and r ≠ 1. For each path Ri in Fr, traverse all the nodes on its path, and try to insert the nodes into the path to which a smaller vehicle type t (t < r) belongs without violating the capacity limit and without increasing the total path cost. After all the paths in Fr have been traversed, it means that the vehicle type r has been traversed at this time, and it is necessary to switch to the next vehicle type r - 1. Determine whether r - 1 is greater than 1 at this time. If it is, continue to traverse the next vehicle type r - 1; otherwise, end the adjustment. After executing the loading volume adjustment strategy, set the number of iterations T to 1 and the number of non-improvement times C to 0.

[0068] Specifically, through the loading volume adjustment strategy LAS, the large vehicle types have more available loading space, increasing the flexibility of subsequent local search adjustments and providing more possibilities for subsequent operator optimization.

[0069] S104: Adjust the improved solution a through the local search strategy to obtain the best solution B_sol.

[0070] Specifically, during the local search process, use four neighborhood operators: one-point move, two-point swap, 2-Opt, and Or-Opt in sequence to find the best solution within the neighborhood, and the operation of these four operators does not involve the station nodes.

[0071] Specifically, the specific descriptions of the above local search operators are as follows:

[0072] One-point move: Select a non-station node on the path, remove it from its original position, and then select a position on the same path or another path to insert it;

[0073] Two-point swap: Select two non-station nodes on the same path or different paths, swap their positions and then insert them;

[0074] 2-Opt: Select two non-intersecting edges in the path and disconnect them. After disconnection, reverse one of the path segments and then reconnect these paths;

[0075] Or-Opt: Select a specified-length path segment from the path and move it to another position on the same path or another path.

[0076] S105: Based on the best solution B_sol, make adjustments through fixed random number perturbations to obtain a new best solution.

[0077] Specifically, the number of disturbances NP is set. At each disturbance, a random node in the random path is selected, and the node is used as a reference to remove b customer nodes in its neighborhood range NR. The number of removed customer nodes b is determined by the disturbance parameter S and the total number of customer nodes, and the removed b customer nodes are temporarily stored in the array. Subsequently, the b customer nodes temporarily stored in the array are inserted into the path in a random order.

[0078] Determine whether the path cost of the new solution obtained after the current disturbance is reduced compared with the best solution before the current disturbance. If it is reduced, use the new solution as the new best solution for the next disturbance. Otherwise, use the best solution before the current disturbance for the next disturbance. Until the number of disturbances NP reaches the preset number, end the disturbance and output the new best solution obtained.

[0079] Specifically, the fixed random number perturbation is set to prevent the solution from falling into the local optimum.

[0080] S106: Based on the above best solution B_sol and the new best solution, calculate the path load percentage variance and path cost. If the path load percentage variance and path cost meet the preset acceptance strategy, accept the new best solution and execute S107; otherwise, return to S104 and repeat.

[0081] Specifically, the path loading percentage variance calculation expression is:

[0082]

[0083] Where n represents the number of paths corresponding to the solution x, n≤N; P i Represents the path R i The corresponding vehicle loading rate is the path R i Corresponding to the ratio of the load on the vehicle to the maximum capacity of the vehicle model, Z represents all paths P1, P2…P n The average loading rate of all vehicles on the road.

[0084] The default acceptance policy is defined as:

[0085]

[0086] Among them, S c represents the best solution B_sol, S n represents the new best solution, F(x) represents the path cost, and f(x) represents the path load percentage variance;

[0087] In this strategy, if the path cost of the new best solution is better than the best solution B_sol, the new best solution is accepted, the best solution B_sol is updated, the number of iterations T is increased by 1, the number of times without improvement C is reset to zero, and the process jumps to S107; if the path cost of the new best solution is not better than the best solution B_sol, but the path load percentage variance (i.e., f(x)) is better than the best solution B_sol, the new best solution is still accepted, the best solution B_sol is updated, the number of iterations T is increased by 1, the number of times without improvement C is reset to 0, and the process jumps to S107; if the path load percentage variance of the new best solution is not better than the best solution B_sol, the number of times without improvement C is increased by 1, and the process jumps to S104 and repeats.

[0088] S107: Determine whether the iteration stop condition is met. If the iteration stop condition is met, execute step 8, otherwise return to step 4 and repeat;

[0089] Correspondingly, the iteration stop condition includes judging whether the number of iterations T and the number of no-improvement times C have reached preset values. If so, S108 is executed; otherwise, the process returns to S104 and repeats the execution.

[0090] Specifically, if the number of iterations reaches the upper limit, it means that the solution has been executed a sufficient number of times. If the number of improvements reaches the upper limit, it means that the solution has little room for improvement.

[0091] It should be noted that after the random number perturbation adjustment, the vehicle load has changed, the corresponding loading rate has changed, and the variance of the path loading percentage is different. The calculation of the path loading percentage variance and the use of the acceptance strategy are to avoid the solution from falling into the local optimum, while ensuring that the distribution of nodes in the newly obtained solution path is more balanced and the loading rate is more stable.

[0092] S108: Optimizing the new best solution B_sol through the vehicle type optimization strategy VOS to obtain the global optimal solution A_sol; the vehicle type optimization strategy VOS is used to optimize the relationship between the path load in the new best solution and the maximum capacity of the corresponding vehicle type.

[0093] Specifically, in order to avoid the situation where a relatively long path is assigned to a higher-cost vehicle model and the final solution cannot be guaranteed to be the minimum-cost solution, the vehicle model optimization strategy VOS is used;

[0094] Specifically, obtain the model set M = {1, 2, ..., m} used by the current new best solution, and traverse from model 1 to model m in ascending order. Set the current model i = 1; then obtain the path set using model i as f i = {R1, R2, ..., R i}, and then obtain the set of paths with all loads not exceeding the maximum capacity of vehicle type i as F I = {R1, R2, ..., R I}. When the number of paths corresponding to vehicle type i is less than the number of paths whose load does not exceed the maximum capacity of vehicle type i, I According to the path length, the paths R1 to R I Sort in descending order, and then assign model i to this part of the path in order. After completing the assignment of model i, you need to move on to the next model i+1. Determine whether i is less than or equal to m. If so, continue to traverse the next model i+1, otherwise end the adjustment.

[0095] Based on the above embodiments, the present invention further provides a vehicle path planning device for vehicles of different vehicle types and multiple cabins based on multi-strategy iterative local search, comprising:

[0096] Data acquisition module: used to acquire vehicle information and node information, and establish a path cost minimization equation. The node information includes customer nodes and station nodes; the vehicle information includes the vehicle model information and the total number of all vehicle models;

[0097] Initial result module: used to obtain the initial path solution C_sol using the cheapest insertion method according to the vehicle information and node information; the cheapest insertion method refers to first generating the first part of the path according to the customer nodes with large demand, removing the part of nodes from the node information, and then randomly selecting some customer nodes from the remaining node information to generate the second part of the path; the first part of the path and the second part of the path constitute the initial path, and the remaining customer nodes are inserted into the initial path to obtain the initial solution; the number of the initial paths is equal to the total number of all vehicle models;

[0098] Loading adjustment module: used to improve and optimize the initial solution C_sol through the loading adjustment strategy LAS, and output the improved solution a; the loading adjustment strategy LAS is used to improve the loading rate of smaller vehicles on the path and optimize the loading space of large vehicles;

[0099] Local search module: used to adjust the improved solution a through the local search strategy to obtain the best solution B_sol;

[0100] Perturbation module: used to adjust the best solution B_sol through fixed random number perturbation to obtain a new best solution;

[0101] The first judgment module is used to calculate the path load percentage variance and path cost based on the best solution B_sol and the new best solution. If the path load percentage variance and path cost meet the preset acceptance strategy, the new best solution is accepted and the second judgment module is entered; otherwise, the local search module is returned to repeat the operation;

[0102] The second judgment module is used to judge whether the iteration stop condition is met; if the iteration stop condition is met, the vehicle model optimization module is entered, otherwise, the local search module is returned to repeat the operation;

[0103] Vehicle type optimization module: used to optimize the new best solution through the vehicle type optimization strategy VOS to obtain the global optimal solution A_sol; the vehicle type optimization strategy VOS is used to optimize the relationship between the path loading capacity in the new best solution and the maximum capacity of the corresponding vehicle type.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 path planning of vehicles with different types and multiple cabins based on multi-strategy iterative local search, characterized in that: Including: Step 1: Obtain vehicle information and node information, and establish a path cost minimization relationship; the node information includes customer nodes and station nodes; the vehicle information includes the vehicle model information used and the total number information of all vehicle models; Step 2: According to the vehicle information and node information, use the cheapest insertion method to obtain an initial path solution C_sol; the cheapest insertion method means that first, generate the first part of the path based on the customer nodes with larger demands, remove these nodes from the node information, and then randomly select some customer nodes from the remaining node information to generate the second part of the path; the first part of the path and the second part of the path form an initial path, and insert the other remaining customer nodes into the initial path to obtain an initial solution; the number of the initial paths is equal to the total number of all vehicle models; Step 3: Improve and optimize the initial solution C_sol through the load adjustment strategy LAS, and output an improved solution a; the load adjustment strategy LAS is used to improve the loading rate of smaller vehicle models on the path and optimize the loading space of larger vehicle models; Step 4: Adjust the improved solution a through the local search strategy to obtain the best solution B_sol; Step 5: Based on the best solution B_sol, make adjustments through fixed random number perturbation to obtain a new best solution; Step 6: Based on the above best solution B_sol and the new best solution, calculate the path load percentage variance and the path cost. If the path load percentage variance and the path cost meet the preset acceptance strategy, accept the new best solution and execute Step 7; Otherwise, return to Step 4 and repeat the execution; Step 8: Determine whether the iteration stop condition is met. If the iteration stop condition is met, execute Step 9, otherwise return to Step 4 and repeat the execution; Step 9: Optimize the new best solution through the vehicle model optimization strategy VOS to obtain the global optimal solution A_sol; the vehicle model optimization strategy VOS is used to optimize the relationship between the path load and the maximum capacity of the corresponding vehicle model in the new best solution.

2. According to claim 1, a method for path planning of vehicles with different types and multiple cabins based on multi-strategy iterative local search, characterized in that: In Step 1, The path cost minimization relationship is: Where F represents the path cost of the corresponding solution; V represents the set of node information; M represents the set of used vehicle models, M = {1, 2, ..., m}; x ijm represents a binary decision variable, x ijm =1 means there is a vehicle of model m going from node i to node j, otherwise x ijm =0;d ij represents the path distance from node i to node j; c m Represents the unit transportation cost of vehicle type m.

3. The method for path planning of vehicles with different types and multiple cabins based on multi-strategy iterative local search according to claim 2 is characterized in that: Step 1 also includes preset required parameters. The initialized required parameters include setting the maximum number of iterations T, the neighborhood size NR, the perturbation parameter S, the number of perturbation times NP, and the acceptance strategy non-improvement times C.

4. The method for path planning of vehicles with different types and multiple cabins based on multi-strategy iterative local search according to claim 3 is characterized in that: In Step 2, the specific steps of using the cheapest insertion method to obtain the path initial solution C_sol include: Count the total number of all models in the model set N, and select from the node information set V in turn according to the demand size client nodes, generating Paths are the first part of the paths, and these customer nodes are removed from the node information set V; randomly select the remaining customer nodes from the node information set V client nodes, generating The paths are the second part of the paths, and these customer nodes are removed from the node information set V; a total of N initial paths are generated, and the vehicle types with maximum loading capacity exceeding the current demand are assigned to the initial paths; based on the N initial paths, under the constraint of meeting the maximum capacity, the remaining customer nodes in the node information set V are inserted into the vehicles that minimize the path cost; if there are customer nodes that cannot be inserted into the initial path, the solution is abandoned and the initial solution is regenerated.

5. The method for path planning of vehicles with different types and multiple cabins based on multi-strategy iterative local search according to claim 3 is characterized in that: In Step 3, the specific steps of the load adjustment strategy LAS include: Get the set of all vehicle models used in the current solution M = {1, 2, ..., m}, traverse all non-minimum vehicle models in order from large to small, and get the set of all paths using vehicle model r Fr = {R1, R2, ..., R i }, where r∈M and r≠1; set the current vehicle model to r=m; For each path Ri in Fr, traverse the customer nodes on its path, and insert the customer nodes into the path of the smaller vehicle model t (where t < r) without violating the capacity limit and without increasing the total path cost; After all the paths in Fr have been traversed, traverse the next vehicle model r - 1; determine whether r - 1 is greater than 1. If so, continue to traverse the vehicle model r - 1; otherwise, end the adjustment; set the number of iterations T to 1 and the non-improvement times C to 0.

6. The method for path planning of vehicles with different types and multiple cabins based on multi-strategy iterative local search according to claim 3 is characterized in that: In Step 5, the fixed random number perturbation specifically includes: Each time of perturbation, randomly select a path from N paths, randomly select a customer node in the selected path as a reference, remove b customer nodes within its neighborhood range NR, and temporarily store them in an array; Insert the client nodes temporarily stored in the array back into the path in a random order; Determine whether the path cost of the new solution obtained after the current disturbance is reduced compared with the best solution before the current disturbance. If it is reduced, use the new solution as the new best solution for the next disturbance; otherwise, use the best solution before the current disturbance for the next disturbance; When the number of perturbations NP reaches the preset number, the perturbation is terminated and the new best solution finally obtained is output.

7. The method for path planning of vehicles with different types and multiple cabins based on multi-strategy iterative local search according to claim 3 is characterized in that: In step 6, the path load percentage variance calculation expression is: Where n represents the number of paths corresponding to the solution x, n≤N; P i Represents the path R i The corresponding vehicle loading rate is the path R i Corresponding to the ratio of the load on the vehicle to the maximum capacity of the vehicle model, Z represents all paths P1, P2…P n The average loading rate of all vehicles on the road.

8. The method for path planning of vehicles with different types and multiple cabins based on multi-strategy iterative local search according to claim 7 is characterized in that: In step 6, The default acceptance strategy is defined as: Among them, S c represents the best solution B_sol, S n represents the new best solution, F(x) represents the path cost, and f(x) represents the path load percentage variance; If the new best solution has a smaller path cost, then accept the new best solution, increase the number of iterations T by 1, and return the number of times the strategy is not improved C to 0; if the path cost of the new best solution remains unchanged or increases, but the variance of the percentage of path load decreases, then accept the new best solution, increase the number of iterations T by 1, and return the number of times the strategy is not improved C to 0; in other cases, do not accept the new best solution, and increase the number of times the strategy is not improved C by 1; Correspondingly, in step 7, the iteration stop condition includes determining whether the number of iterations T and the number of no-improvement times C have both reached preset values, and if so, executing step 8; otherwise, returning to step 4 and repeating.

9. The method for path planning of vehicles with different types and multiple cabins based on multi-strategy iterative local search according to claim 3 is characterized in that: In step 8, the vehicle type optimization strategy VOS specifically includes: Get the model set M = {1, 2, ..., m} used by the current new best solution, and traverse from model 1 to model m in ascending order; set the current model to i, and get the path set f using model i i = {R1, R2, ..., R i } and the set of all paths whose load does not exceed the maximum capacity of vehicle type i I = {R1, R2, ..., R I }; When the number of paths corresponding to vehicle type i is less than the number of paths whose load does not exceed the maximum capacity of vehicle type i, for set F I Path R1 to Path R I Sort in descending order according to the path length, and then assign model i to this part of the path in order; after completing the assignment of model i, it is necessary to move on to the next model i+1; determine whether i is less than or equal to m, if so, continue to traverse the next model i+1, otherwise end the adjustment.

10. A vehicle path planning device for different vehicle types and multiple cabins based on multi-strategy iterative local search, characterized in that: include: Data acquisition module: used to acquire vehicle information and node information, and establish a path cost minimization equation. The node information includes customer nodes and station nodes; the vehicle information includes the vehicle model information and the total number of all vehicle models; Initial result module: used to obtain the initial path solution C_sol using the cheapest insertion method according to the vehicle information and the node information; the cheapest insertion method refers to first generating the first part of the path according to the customer nodes with large demand, removing the part of the nodes from the node information, and then randomly selecting part of the customer nodes from the remaining node information to generate the second part of the path; the first part of the path and the second part of the path form the initial path, and the remaining customer nodes are inserted into the initial path to obtain the initial solution; The number of the initial paths is equal to the total number of all vehicle models; Loading adjustment module: used to improve and optimize the initial solution C_sol through the loading adjustment strategy LAS, and output the improved solution a; the loading adjustment strategy LAS is used to improve the loading rate of smaller vehicles on the path and optimize the loading space of large vehicles; Local search module: used to adjust the improved solution a through the local search strategy to obtain the best solution B_sol; Perturbation module: used to adjust the best solution B_sol through fixed random number perturbation to obtain a new best solution; The first judgment module is used to calculate the path load percentage variance and path cost based on the best solution B_sol and the new best solution. If the path load percentage variance and path cost meet the preset acceptance strategy, the new best solution is accepted and the second judgment module is entered; otherwise, the local search module is returned to repeat the operation; The second judgment module is used to judge whether the iteration stop condition is met; if the iteration stop condition is met, the vehicle model optimization module is entered, otherwise, the local search module is returned to repeat the operation; Vehicle type optimization module: used to optimize the new best solution through the vehicle type optimization strategy VOS to obtain the global optimal solution A_sol; the vehicle type optimization strategy VOS is used to optimize the relationship between the path loading capacity in the new best solution and the maximum capacity of the corresponding vehicle type.

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