Transport route generation method, electronic device, storage medium and computer program product

Abstract

Provided in the present application are a transport route generation method, an electronic device, a storage medium and a computer program product. The method comprises: on the basis of a plurality of transport routes in a transport network, generating a plurality of initial route sets; according to a target optimization strategy, performing a plurality of optimizations on the plurality of initial route sets, so as to obtain a plurality of optimization results, wherein the target optimization strategy comprises reallocating a set of routes to be optimized to which a target transport route belongs, and the target transport route comprises a target route and at least one transport route, the distance from which to the target route is less than a target distance threshold value; and according to an optimal result among the plurality of optimization results, outputting transport routes in a first-type route set in original states thereof, adjusting transport routes in a second-type route set to transport routes in a hub-and-spoke mode, and outputting same. Compared with a manual route planning mode, it is possible to process a larger and more complex logistics network, and it is possible to complete route planning more quickly.

Description

Methods for generating transportation routes, electronic equipment, storage media, and computer program products. Technical Field

[0001] This application relates to the field of logistics and transportation technology, specifically to a method for generating transportation routes, electronic equipment, storage media, and computer program products. Background Technology

[0002] The design of a logistics network directly affects the efficiency, cost, and environmental impact of goods delivery. The hub-and-spoke model, a commonly used logistics network organization, optimizes logistics activities through the collaboration of central nodes (Hubs) and radiating nodes (Spokes). In this model, the logistics activities of the radiating nodes are centrally processed through the central node. As shown in Figure 1, taking a pickup scenario as an example, when using the hub-and-spoke model, goods are transported from the radiating nodes to the central node, where they undergo aggregation and other processing before being transported to their destination through the central node.

[0003] Conventional methods for building hub-and-spoke logistics networks typically assume that all routes can be replanned. Therefore, this approach is suitable for planning a completely new, unserved logistics network in a new region. When applied to an already operational, mature logistics network, significant route adjustments can have a major impact on operations and present management challenges. Furthermore, manually adjusting transportation routes within the logistics network is also time-consuming, and the results are often unreliable, leading to low vehicle utilization. Summary of the Invention

[0004] This application provides a method for generating transportation routes, an electronic device, a storage medium, and a computer program product to solve the problem of excessively long cabling time.

[0005] The first aspect of this application provides a method for generating transportation routes. The method includes: generating multiple initial route sets based on multiple transportation routes in a transportation network; wherein each initial route set includes at least one transportation route; optimizing the multiple initial route sets multiple times according to a target optimization strategy to obtain multiple optimization results; wherein each optimization after the first optimization determines the previous optimization result as the route set to be optimized for further optimization; the target optimization strategy includes: reallocating the route set to be optimized to which the target transportation route belongs, and creating a new route set to be optimized for the target transportation routes that failed to be allocated; the target transportation route includes the target route and at least one transportation route whose distance to the target route is less than a target distance threshold; the target route is one of the multiple transportation routes, and the target route is different for each optimization; according to the best result among the multiple optimization results, outputting the transportation routes in the first type of route set as they are, and adjusting the transportation routes in the second type of route set into a center-radial mode and outputting them.

[0006] Optionally, the number of transport routes in the first type of route set is one, and the number of transport routes in the second type of route set is multiple.

[0007] Optionally, the multiple initial route sets are optimized multiple times according to the target optimization strategy to obtain multiple optimization results, including: for each optimization, determining the target transportation route from the multiple initial route sets; for each target transportation route, moving M transportation routes from the same route set to be optimized, including the target transportation route, into the resource pool to be allocated; wherein M is greater than or equal to 1, and the route set to be optimized is the optimization result of the previous optimization or the initial route set; with the goal of minimizing the incremental transportation cost, inserting all transportation routes in the resource pool to be allocated into the remaining route set to obtain the optimization result, wherein the remaining route set is the route set generated after the route set to be optimized is removed from the M transportation routes.

[0008] Optionally, for each target transportation route, after moving M transportation routes, including the target transportation route, from the same set of routes to be optimized into the resource pool to be allocated, the method further includes: for each remaining set of routes after the M transportation routes are removed, electing a center node according to the center-radial pattern, and if the election fails, moving all transportation routes in the remaining set of routes that failed the election into the resource pool to be allocated.

[0009] Optionally, with the goal of minimizing the incremental transportation cost, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain the optimization result. This includes: performing steps A1 to A2 for each transportation route in the resource pool to be allocated until all are inserted into the set of remaining routes to obtain the optimization result; A1, traversing each set of remaining routes, generating a random number within the target value range when the traversed set meets the target conditions, and updating the currently traversed set of remaining routes to the optimal insertion set when the random number is greater than a preset threshold; wherein, the target conditions include: after inserting the currently traversed set of remaining routes, a center node can be elected according to the center-radial model, and the incremental transportation cost is smaller; the preset threshold is within the target value range; A2, after traversing all sets of remaining routes, inserting the currently targeted transportation route into the optimal insertion set, and inserting the currently targeted transportation route into a newly created set of routes to be optimized when the optimal insertion set is empty.

[0010] Optionally, with the goal of minimizing the incremental transportation cost, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain the optimization result, including: with the goal of minimizing the incremental transportation cost, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain a new solution; based on the annealing algorithm or the hill climbing algorithm, it is determined whether to accept the new solution as the current solution, and if the new solution is accepted as the current solution, the new solution is determined as the optimization result.

[0011] Optionally, the method further includes: if a new solution is accepted as the current solution, determining whether to accept the new solution as the optimal solution based on an annealing algorithm or a hill-climbing algorithm; if the new solution is accepted as the optimal solution, updating the optimal solution using the optimization results; correspondingly, the optimal result among multiple optimization results is the optimal solution in multiple optimization processes.

[0012] Optionally, determining the target transportation route from multiple initial route sets includes: randomly selecting one transportation route from multiple initial route sets as the target route; selecting at least one transportation route whose distance from the target route is less than a target distance threshold; and using the selected target route and at least one transportation route as the target transportation route.

[0013] Optionally, the transportation route in the center-radial mode includes: a central node and radial nodes, wherein each radial node has a cargo transportation relationship with the central node.

[0014] Optionally, outputting the transport routes in the first type of route set as they are, and adjusting the transport routes in the second type of route set to transport routes in a center-radial mode and outputting them, includes: outputting the transport routes in the first type of route set as they are, adjusting the transport routes in the second type of route set to transport routes in a center-radial mode, and outputting them through the display device.

[0015] A second aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for generating a transport route of the first aspect.

[0016] A third aspect of this application provides a storage medium storing a computer program, which, when executed by a processor, provides a method for generating a transport route according to the first aspect.

[0017] A fourth aspect of this application provides a computer program product, including: a computer program, and the method for generating a transport route as described in the first aspect when the computer program is executed by a processor.

[0018] As can be seen from the above technical solution, the embodiments of this application can first automatically generate an initial set of routes for the logistics network, and then continuously optimize the initial set of routes through multiple optimizations. The final optimal optimization result is obtained, namely, a first type of route set that requires the original output of transportation routes, and a second type of route set that requires the transportation routes to be adjusted according to a center-radial pattern. By analyzing routes in the logistics network that can be adjusted and have positive benefits after adjustment, these are converted to a center-radial pattern; routes that cannot be adjusted or do not have positive benefits after adjustment are maintained in their original state. This method reduces the risk of large-scale adjustments and enhances the algorithm's adaptability and reliability to complex environments. At the same time, compared with manual route planning, it can handle larger and more complex logistics networks and complete route planning more quickly. Furthermore, it avoids the arbitrariness and subjectivity of manual route planning, making the results more reliable. Further, during each optimization, transportation routes with relatively concentrated geographical locations are reconstructed, thereby obtaining the optimal optimization result faster and reducing the number of optimizations. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 is a schematic diagram of the logistics network topology under the center-radial model.

[0021] Figure 2 is one of the flowcharts of a method for generating a transportation route according to an embodiment of this application.

[0022] Figure 3 is a schematic diagram of the initial line set in an embodiment of this application.

[0023] Figure 4 is a schematic diagram of the set of lines to be optimized in the embodiments of this application.

[0024] Figure 5 is one of the schematic diagrams of removing the target transportation route in the embodiments of this application.

[0025] Figure 6 is a second schematic diagram of removing the target transportation route in an embodiment of this application.

[0026] Figure 7 is a schematic diagram of the remaining line set in an embodiment of this application.

[0027] Figure 8 is a schematic diagram of inserting the remaining line set in an embodiment of this application;

[0028] Figure 9 is a second flowchart of a method for generating a transportation route according to an embodiment of this application.

[0029] Figure 10 is a schematic diagram of the structure of a transportation route generation device provided in an embodiment of this application.

[0030] Figure 11 is a schematic diagram of the electronic device structure provided in an embodiment of this application. Detailed Implementation

[0031] Unless otherwise defined, the technical or scientific terms used in the embodiments of this specification shall have the ordinary meaning understood by one of ordinary skill in the art to which this specification pertains. The terms "first," "second," and similar terms used in the embodiments of this specification do not indicate any order, quantity, or importance, but are merely used to avoid confusion of constituent elements.

[0032] Unless the context otherwise requires, throughout this specification, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, that is, "including, but not limited to." In the description of this specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this specification. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0033] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0034] Overview

[0035] As described in the background section, manual route planning has several pain points. For example, planners need to consider complex and ever-changing constraints one by one, which is extremely time-consuming. Route generation methods based on human experience often only achieve locally good results, are costly, and are prone to violating some operational constraints, leading to delays. After the route planning results are put into operation, planners find it difficult to respond and make adjustments in a timely manner to fluctuations in demand and network expansion, resulting in low utilization rates for some vehicles. At the same time, applying the center-radial model to an already operational logistics network would require significant adjustments, which is not conducive to cost savings.

[0036] Although automated routing solutions using computer equipment are gradually emerging in the market, and the routing process can be viewed as a process of finding the optimal solution, the algorithms used in existing solutions have not been specifically adapted for logistics networks, resulting in a still very time-consuming solution process.

[0037] To address the aforementioned problems, the inventors of this application propose using a set to differentiate transportation routes during the automatic route planning process using computer equipment, considering grouping transportation routes that can be adjusted to a center-radial pattern into the same set. When optimizing the initial solution or initial route set, optimization can be targeted based on the actual situation of the logistics network. Therefore, this application provides a method for generating transportation routes, aiming to automatically generate an initial route set for the logistics network first, and then continuously optimize the initial route set through multiple optimizations to finally obtain the optimal optimization result, namely, a first type of route set that requires the transportation routes to be output in their original state, and a second type of route set that requires the transportation routes to be adjusted according to the center-radial pattern. By analyzing and identifying routes in the logistics network that can be adjusted and have positive benefits after adjustment, these routes are converted to the center-radial pattern; routes that cannot be adjusted or do not have positive benefits after adjustment are maintained in their original state. This method reduces the risks associated with large-scale adjustments and enhances the algorithm's adaptability and reliability to complex environments. Furthermore, compared to manual route planning, it can handle larger and more complex logistics networks and complete route planning more quickly. Furthermore, this approach avoids the arbitrariness and subjectivity of manual route planning, ensuring more reliable results. Moreover, each optimization attempt reconstructs transportation routes with concentrated geographical locations, leading to faster attainment of optimal results and reducing the number of optimization cycles. The following examples illustrate the specific solution in detail.

[0038] Exemplary methods

[0039] This application provides an exemplary description of a method for generating transportation routes, as shown in Figure 2. The method for generating transportation routes includes:

[0040] S201: Generate multiple initial route sets based on multiple transport routes in the transport network.

[0041] It should be noted that a transportation network, also known as a logistics network, is a network composed of logistics nodes used to realize logistics transportation. In some embodiments, the logistics network can be a network composed of logistics nodes in a designated business area. Each logistics node can be considered a service point. In a consolidated cargo scenario, the service point is the origin of the goods. In a bulk cargo scenario, the service point is the destination of the goods. Goods may pass through one or more service points during transportation within the logistics network.

[0042] In this embodiment, each initial route set includes at least one transportation route. Optionally, an initial route set can be created for each transportation route, meaning that each initial route set contains a single transportation route. As shown in Figure 3, when the transportation routes in the logistics network include four transportation routes ABX, CX, DX, and EX, then four initial route sets G1, G2, G3, and G4 are generated respectively.

[0043] In some embodiments, initial transportation routes can be generated by combining business information involved in the logistics network. This business information includes, but is not limited to, information related to each service point. Examples include cargo information, geographical information, vehicle type information, and constraints. Cargo information may include, for example, the origin of the cargo, the destination of the cargo, the weight of the cargo, the available departure time of the cargo, and the latest arrival time of the cargo. Geographical information may include, for example, the travel time and distance between each logistics node. Vehicle type information may include, for example, vehicle loading limits and the duration of vehicle stay at the service logistics node. Constraints may include, for example, the time of cargo loading being after its earliest available departure time, the time of cargo arrival at its destination being before its latest arrival time, and the total weight of cargo served by each transportation route not exceeding the capacity of the vehicle type.

[0044] S202: Optimize multiple initial route sets multiple times according to the target optimization strategy to obtain multiple optimization results.

[0045] It should be noted that during the initial optimization, the initial set of routes is used as the set of routes to be optimized. Each subsequent optimization uses the result of the previous optimization as the set of routes to be optimized. The purpose of this step is to re-divide the sets to which each transport route belongs, grouping those routes that can be adjusted and will generate positive revenue after adjustment together, thus facilitating their conversion to a center-radial model. Routes that cannot be adjusted or will not generate positive revenue after adjustment are separated and their original state is maintained. In some embodiments, an optimization result is obtained during each optimization process. Each optimization result can be understood as the result of attempts in the process of finding the optimal partitioning method. By continuously trying to find the optimal partitioning method, an optimal result is obtained as the optimal partitioning method, so that the transport routes in the transport network can be adjusted according to the optimal partitioning method in the future. For the conditions for ending the optimization, a maximum number of optimization attempts can be set; optimization stops when the maximum number of optimization attempts is reached. Alternatively, a maximum optimization duration can be set; optimization stops when the cumulative optimization duration reaches the maximum optimization duration.

[0046] The target optimization strategy includes: reallocating the target transportation route to the set of routes to be optimized, and creating new sets of routes to be optimized for target transportation routes that failed to be allocated; the target transportation route includes the target route and at least one transportation route whose distance to the target route is less than the target distance threshold; the target route is one of multiple transportation routes, and the target route is different each time it is optimized. In some embodiments, when calculating the distance between any two transportation routes, the distance between transportation route a and transportation route b can be defined as the minimum distance between the service points of transportation route a and transportation route b. For example, if the route of transportation route a is A->B->X, and the route of transportation route b is C->D->Y, where A, B, C, and D are service points, and X and Y are destinations, then the distance between transportation route a and transportation route b can be min{dist(A,C),dist(A,D),dist(B,C),dist(B,D)}, where the dist function represents the distance between points, and min indicates taking the minimum value.

[0047] It's worth noting that the target distance threshold can be a pre-set threshold. Here, situations where the distance between transport routes is less than the target distance threshold can be considered as the transport routes being close together or clustered. Therefore, in each optimization, selecting some clustered transport routes as the optimization direction can quickly approach the optimal result and reduce the number of optimization iterations.

[0048] S203: Based on the optimal result among multiple optimization results, output the original state of the transportation routes in the first type of route set, and adjust the transportation routes in the second type of route set into transportation routes in the center-radial mode and output them.

[0049] It should be noted that each optimization result is an attempt to find the optimal partitioning method, representing a partitioning method for the set to which transportation routes belong. Therefore, by transporting goods in the logistics network according to each optimization result, the transportation cost can be calculated. By comparing transportation costs, the optimal transportation cost can be obtained, for example, the optimization result with the minimum transportation cost. This result is then taken as the optimal optimization result. In some embodiments, the optimal result can be recorded and updated during each optimization process, so that after multiple optimizations, the optimal result can be directly used for subsequent processing.

[0050] In this embodiment, the number of transportation routes in the first type of route set is one, and the number of transportation routes in the second type of route set is multiple. The center-radial mode will not be detailed here. It is understood that the transportation routes in the center-radial mode include: a central node and radiating nodes, wherein each radiating node has a cargo transportation relationship with the central node.

[0051] In some embodiments, for example, the transport routes in the first type of route set are output as is via a display device, while the transport routes in the second type of route set are adjusted to transport routes in a center-radial mode and output for viewing by personnel.

[0052] In this embodiment, an initial set of routes for the logistics network can be automatically generated first, and then continuously optimized through multiple optimizations. The final optimal optimization result is obtained, namely, a first-type set of routes that need to be output as is, and a second-type set of routes that need to be adjusted according to a center-radial pattern. Analysis identifies routes in the logistics network that can be adjusted and will generate positive returns after adjustment, and these are converted to a center-radial pattern; routes that cannot be adjusted or will not generate positive returns after adjustment are maintained as is. This approach reduces the risks associated with large-scale adjustments and enhances the algorithm's adaptability and reliability to complex environments. Furthermore, compared to manual route planning, it can handle larger and more complex logistics networks and complete route planning more quickly. Additionally, it avoids the arbitrariness and subjectivity of manual route planning, ensuring more reliable results. Moreover, each optimization reconstructs geographically concentrated transportation routes, thereby obtaining the optimal optimization result faster and reducing the number of optimization iterations.

[0053] In some embodiments of this application, the optimization process is the same each time, all following the target optimization strategy. To reduce repetition, only one optimization process is described here as an example. In this embodiment, the process of optimizing multiple initial route sets according to the target optimization strategy to obtain an optimization result includes:

[0054] The target transportation route is determined from multiple initial route sets. For each target transportation route, M transportation routes from the same set of routes to be optimized, including the target transportation route, are moved into the resource pool to be allocated. Here, M is greater than or equal to 1, and the set of routes to be optimized is the optimization result of the previous optimization or the initial route set. With the goal of minimizing the incremental transportation cost, all transportation routes in the resource pool to be allocated are inserted into the remaining route set to obtain the optimization result. Here, the remaining route set is the route set generated after the set of routes to be optimized is removed from the set of routes to be optimized by M transportation routes.

[0055] It should be noted that the relevant descriptions of the above embodiments are provided for the target transportation route, and will not be repeated here. Furthermore, this application does not limit the method for determining the target transportation route from multiple initial route sets.

[0056] Understandably, the reallocation of the target transportation route's set of routes to be optimized involves two steps: dismantling and rebuilding. The dismantling step involves moving the dismantled transportation routes from their original set into the resource pool to be allocated. For example, as shown in Figure 4, assume the logistics network consists of ten service points (radiating nodes) A, C, D, E, F, H, J, and L, two central nodes B and K, and two destinations X and Y. The sets of routes to be optimized include G1 and G2, with transportation routes ABX, CX, DX, and EX in G1, and transportation routes FY, JLY, and HDY in G2. During dismantling, if EX and HDY are identified as target transportation routes, then when dismantling EX, DX in G1 can be dismantled simultaneously. As shown in Figure 5, EX and DX are moved into the resource pool S to be allocated. When dismantling HDY, it can be dismantled only from G2, as shown in Figure 6, where HDY is moved into the resource pool S to be allocated.

[0057] The reconstruction step involves rationally re-inserting the dismantled or unallocated resource pool transportation lines into the set of lines to be optimized. For example, as shown in Figure 7, after dismantling, the remaining set of lines G1 and G2 are obtained, along with the unallocated resource pool S. G1 includes two transportation lines, ABX and CX. G2 includes three transportation lines, DX, EX, and FX. S includes one transportation line, KX. If KX is inserted into G1, the optimization result is shown in Figure 8. It is worth noting that the optimization objective of this application is to find the optimal partitioning method; therefore, the incremental transportation cost needs to be considered when inserting transportation lines. The reconstruction step is performed with the objective of minimizing the incremental transportation cost.

[0058] In this embodiment, when dismantling a transportation route, M transportation routes from the same set of routes to be optimized, including the target transportation route, can be dismantled. By reasonably controlling the value of M, the optimization time can be shortened while avoiding damage to the optimized set of transportation routes.

[0059] To improve optimization efficiency, for each target transportation route, after moving M transportation routes, including the target transportation route, from the same set of routes to be optimized into the resource pool to be allocated, the method further includes:

[0060] For each set of remaining lines after removing M transport lines, elect a central node according to the center-radial model. If the election fails, move all transport lines in the set of remaining lines that failed to be elected into the resource pool to be allocated.

[0061] It should be noted that electing a central node means selecting one from the service points of the same set of remaining routes as the central node in the center-radial model. In some embodiments, the central node is elected as follows: First, traverse e∈Ri (Ri is any set of remaining routes). If e satisfies three conditions, then e may be the central node: (i) the distance from other service points besides e to e does not exceed the coverage radius of the central node; (ii) the cargo volume from other service points besides e to e does not exceed the capacity of one vehicle; (iii) after the cargo is transported from other service points besides e to e, it is then transported to the destination, and the arrival time at the destination is before the latest arrival time of the cargo. Second, if e may be the central node, calculate the cost of the combination under the hypothetical case of e as the central node. The value of this cost is equal to the sum of the following three: (i) the cost from other service points besides e to e; (ii) the cost of cargo aggregation processing at e; (iii) the cost from e to the destination. If there are multiple e that satisfy the conditions, then the e corresponding to the lowest cost is selected as the central node.

[0062] In this embodiment of the application, all transport routes in the remaining set of routes that failed the election are moved into the resource pool to be allocated, which can improve and optimize the process.

[0063] In some embodiments of this application, with the objective of minimizing the incremental transportation cost, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain an optimized result, including:

[0064] Steps A1 to A2 are executed for each transportation route in the resource pool to be allocated until all routes are inserted into the remaining route set to obtain the optimized result.

[0065] A1. Traverse each set of remaining routes. If the traversed set of remaining routes meets the target conditions, generate a random number within the target value range. If the random number is greater than a preset threshold, update the currently traversed set of remaining routes to the optimal insertion set. The target conditions include: after inserting into the currently traversed set of remaining routes, a central node can be elected according to the center-radial model, and the increase in transportation cost is smaller; the preset threshold is within the target value range.

[0066] A2, after traversing all remaining route sets, inserts the currently targeted transportation route into the optimal insertion set, and if the optimal insertion set is empty, inserts the currently targeted transportation route into the newly created set of routes to be optimized.

[0067] It should be noted that the process of inserting each transportation line in the resource pool to be allocated into the set of remaining lines is the same. To reduce duplication, only one insertion process is used as an example here for illustration. There are usually multiple sets of remaining lines. In the embodiments of the present application, with the goal of minimizing the increment of transportation cost, the most suitable set of remaining lines for insertion is searched. To avoid falling into the situation of local optimal solution, this embodiment sets a sub-optimal insertion criterion, that is, the criterion sets a threshold p. If a feasible set of remaining lines is found, the set of remaining lines is skipped with probability p. This method can skip the relatively better set of remaining lines with a certain probability and select the sub-optimal set of remaining lines, thus avoiding falling into the local optimal solution.

[0068] In some embodiments, for any transportation line a in the resource pool to be allocated, first, the original cost of a can be calculated and denoted as z. Then initialize, let the optimal increment of transportation cost d* = z, the index i* of the optimal set of remaining lines to be inserted = -1, and traverse each set of remaining lines Gi ∈ G. Let Gi’ = Gi ∪ {a}. Elect the central node of Gi’. If the election result is successful, calculate the increment of transportation cost d caused by the insertion. If d < d*, a better insertable position is found. Generate a random number r between 0 and 1. If r is greater than p, let d* = d, i* = i. If i* is not equal to -1, it indicates that there is a set of remaining lines that can be inserted. Let the optimal set of remaining lines to be inserted Gi* = Gi* ∪ {a}. Otherwise, it indicates that there is no set of remaining lines that can be inserted, and a new set needs to be created for a, and the only element of this set is this transportation line.

[0069] In the embodiments of the present application, a threshold is set. If a set of remaining lines that can be inserted is found, the set of remaining lines is skipped with this threshold, so that the relatively better set of remaining lines can be skipped with a certain probability and the sub-optimal set of remaining lines can be selected, thus avoiding falling into the local optimal solution.

[0070] In some embodiments of the present application, with the goal of minimizing the increment of transportation cost, all transportation lines in the resource pool to be allocated are inserted into the set of remaining lines, and the optimization results include:

[0071] With the goal of minimizing the increment of transportation cost, all transportation lines in the resource pool to be allocated are inserted into the set of remaining lines to obtain a new solution; based on the annealing algorithm or the hill-climbing algorithm, it is determined whether to accept the new solution as the current solution, and when the new solution is accepted as the current solution, the new solution is determined as the optimization result.

[0072] It should be noted that multiple optimizations of multiple initial line sets according to the target optimization strategy can be regarded as a process of continuously solving the optimal solution according to the specified algorithm. Each optimization result obtained each time is a solution. And the optimal solution is the optimal partitioning method of the set to which the transportation lines belong.

[0073] The algorithms here can be the Hill Climbing algorithm or the Simulated Annealing algorithm. Among them, the Hill Climbing algorithm is an optimization method based on local search, which conducts a small-scale search around the current solution and moves to a better neighboring solution. It determines whether to accept a new solution by comparing the objective function values between adjacent points in the surrounding neighborhood and the current point. If there is no better solution in the neighborhood, the search is terminated. The Simulated Annealing algorithm starts from a relatively high initial temperature. Along with the continuous decrease of the temperature parameter, it combines the probability jump characteristic to randomly search for the global optimal solution of the objective function in the solution space, that is, it can probabilistically jump out of the local optimal solution and finally tend to the global optimal solution. The Simulated Annealing algorithm is an optimization algorithm with a serial structure that endows the search process with a time-varying and ultimately zero-probability jump property, thereby effectively avoiding falling into local minima and finally tending to the global optimal solution.

[0074] Here, only the Simulated Annealing algorithm is taken as an example to illustrate the process of determining whether to accept a new solution as the current solution. For example, let q(s) be a function for calculating the cost of a solution s. Denote the newly generated solution as s'. Denote the known current solution as sc. Then, the process of determining whether to accept s' as the current solution includes: generating a random number u between 0 and 1. If q(s') < q(sc) - T × log(u), then accept the new solution as the current solution and let the current solution sc = s'. Here, log is the natural logarithm function, and T represents the current temperature.

[0075] In the embodiments of this application, by using the Simulated Annealing algorithm or the Hill Climbing algorithm to determine whether to accept a new solution as the current solution, the occurrence of local optimal situations can be avoided as much as possible.

[0076] In some embodiments of this application, in order to quickly determine the optimal result from multiple optimization results, the method further includes: in the case of accepting a new solution as the current solution, based on the annealing algorithm or the Hill Climbing algorithm, determining whether to accept the new solution as the optimal solution; in the case of accepting the new solution as the optimal solution, updating the optimal solution with the optimization result; correspondingly, the optimal result among multiple optimization results is the optimal solution in multiple optimization processes.

[0077] It should be noted that each optimization process will decide whether to update the optimal solution according to the actual situation. After the optimization is completed, the optimal solution is directly used as the optimal result among multiple optimization results. For example, continuing with the above example of determining whether to accept s' as the current solution, when determining whether to accept s' as the optimal solution, if q(s') < q(s*), then accept s' as the optimal solution and let the optimal solution s* = s'. It should be noted that in the Simulated Annealing algorithm, a cooling operation is also required, that is, let T = T × t. Here, T represents the current temperature, and t represents the cooling factor.

[0078] In this embodiment of the application, the optimal result can be quickly determined from multiple optimization results by utilizing the recording and updating mechanism of the optimal solution.

[0079] In some embodiments of this application, determining a target transport route from a plurality of initial route sets includes:

[0080] Randomly select one transportation route from multiple initial route sets as the target route; select at least one transportation route whose distance from the target route is less than the target distance threshold; use the selected target route and at least one transportation route as the target transportation route.

[0081] It should be noted that during each optimization process, different elements can be selected from multiple initial route sets as target transportation routes. In this embodiment, the selection of the first target transportation route (target route) is initiated by random selection. After determining the target route, the geographical location information between each transportation route can be used to easily identify transportation routes that meet the distance requirements. These transportation route points that meet the distance requirements and the target route are then used as the target transportation routes.

[0082] In this embodiment of the application, by randomly selecting the target route, different elements can be selected from multiple initial route sets as the target transportation route in each optimization process.

[0083] To facilitate understanding, the following example, a cargo consolidation scenario, will be used to illustrate the method for generating transportation routes provided in this application. As shown in Figure 9, it includes:

[0084] S901: Information Collection. This information refers to the current route information of the logistics network. Let the set of current transportation routes be {a1,...,an}. Each route passes through multiple service points and has one destination. For example, route ai is A->B->X, indicating that it starts from service point A, passes through service point B, and its destination is X.

[0085] S902: Construct the initial solution. This initial solution is equivalent to the initial route set in the above embodiments. For example, a set T = {G1,...,Gn} can be established, where Gi = {ai}, that is, Gi is a set containing only one element, ai. ai represents a transportation route. Gi is equivalent to the initial route set. Initialize the resource pool to be allocated. Let the current solution be sc and the optimal solution be s*. Initialize the simulated annealing temperature T and cooling factor t.

[0086] S903: Destroy the routes. For example, a transportation route a can be randomly selected. Find the M routes closest to a and form a set Z. Traverse u ∈ Z. If u is already in the resource pool S to be allocated, skip this u. Find the combination Gi where u is located. Generate a random number n in the range (0, card(Gi)]. Randomly select a subset from Gi with a cardinality of n and containing u. Remove the transportation routes of this subset from Gi and put these transportation routes into the resource pool S to be allocated. Re-elect the central node of Gi. If the election result is failure, remove all the remaining transportation routes in Gi and put them into the resource pool S. If the number of routes in the resource pool S to be allocated has reached the upper limit N, terminate the destruction of the routes.

[0087] S904: Reconstruct the routes. For example, reallocate the transportation routes in the resource pool S to be allocated into the sets to construct a new feasible solution. Among them, the basis for determining which set to allocate / insert the transportation route into is to minimize the increment of transportation cost. The increment of transportation cost is equal to the difference between the transportation cost after insertion and the transportation cost before insertion. The set with the smallest cost increment is the optimal insertable set. In particular, if there is no insertable set, a separate set is created for the transportation route. Specifically, the service points in the resource pool S to be allocated can be sorted. Traverse a ∈ S. First, the original cost of a can be calculated and denoted as z. Then initialize, let the optimal transportation cost increment d* = z, the index i* of the optimal remaining route set to be inserted = -1, and traverse each remaining route set Gi ∈ G. Let Gi’ = Gi ∪ {a}. Elect the central node of Gi’. If the election result is successful, calculate the increment d of the transportation cost caused by the insertion. If d < d*, a better insertable position is found. Generate a random number r between 0 and 1. If r is greater than p, let d* = d, i* = i. If i* is not equal to -1, it indicates that there is a remaining route set that can be inserted. Let the optimal remaining route set Gi* to be inserted = Gi* ∪ {a}. Otherwise, it indicates that there is no remaining route set that can be inserted, and a new set needs to be created for a. The only element of this set is this transportation route.

[0088] S905: Determine whether to accept the new solution as the current solution. If so, execute S906; if not, execute S909. This process is the same as the process of whether to accept the new solution as the current solution in the above embodiment and will not be elaborated here.

[0089] S906: Update the current solution and update the current solution to the new solution.

[0090] S907: Determine whether to accept the new solution as the optimal solution. If so, execute S908; if not, execute S909. This process is the same as the process of whether to accept the new solution as the optimal solution in the above embodiment and will not be elaborated here.

[0091] S908: Update the optimal solution by replacing the optimal solution with the new solution.

[0092] S909: Determine if the termination condition is met. If yes, proceed to S910; otherwise, proceed to S903. This termination condition may include the number of optimization attempts (the number of times the optimal solution is updated) or the optimization time (the runtime of the solution process).

[0093] S910: Output the optimal solution. The optimal solution is s*, which includes multiple sets. Each set Gi can be traversed. If Gi contains only one transport route, it is considered that this transport route cannot form a center-radial pattern with other transport routes. Output the original state of this transport route. If Gi contains two or more transport routes, it is considered that the transport routes in Gi can be transformed into a center-radial pattern. Perform an election operation on Gi to determine which service point becomes the center node and which service points become the radial nodes. Output the transport routes of Gi after being modified to ship according to the center-radial pattern.

[0094] Compared to manual route planning, the embodiments of this application are less costly and avoid getting trapped in local optima. Because the algorithm strictly adheres to given constraints and does not rely on human subjective judgment, it avoids the arbitrariness and subjectivity of manual planning. Through computer computation, hundreds or even thousands of transportation routes can be planned within minutes, making it more efficient. In particular, transportation routes with the potential to become hub-and-spoke models are selectively adjusted. Routes unsuitable for hub-and-spoke models are left as is. This strategy avoids unnecessary large-scale modifications, making the route optimization scheme easier to implement.

[0095] Exemplary device

[0096] In this embodiment of the application, a transportation route generation apparatus is also provided, as shown in FIG10. The transportation route generation apparatus includes:

[0097] The first generation module 1001 is used to generate multiple initial route sets based on multiple transportation routes in the transportation network; wherein each initial route set includes at least one transportation route.

[0098] The optimization module 1002 is used to perform multiple optimizations on multiple initial route sets according to the target optimization strategy, resulting in multiple optimization results. Each subsequent optimization after the first optimization uses the previous optimization result as the route set to be optimized. The target optimization strategy includes: reallocating the target transportation route to the route set to be optimized, and creating new route sets to be optimized for target transportation routes that failed to be allocated. The target transportation route includes the target route and at least one transportation route whose distance to the target route is less than a target distance threshold. The target route is one of multiple transportation routes, and the target route is different for each optimization.

[0099] The second generation module 1003 is used to output the original state of the transportation routes in the first type of route set according to the optimal result among multiple optimization results, and to adjust the transportation routes in the second type of route set into transportation routes in the center-radial mode and output them; wherein, the number of transportation routes in the first type of route set is one, and the number of transportation routes in the second type of route set is multiple.

[0100] In some embodiments of this application, the process by which the optimization module 1002 optimizes multiple initial line sets according to a target optimization strategy to obtain an optimization result includes:

[0101] The target transportation route is determined from multiple initial route sets. For each target transportation route, M transportation routes from the same set of routes to be optimized, including the target transportation route, are moved into the resource pool to be allocated. Here, M is greater than or equal to 1, and the set of routes to be optimized is the optimization result of the previous optimization or the initial route set. With the goal of minimizing the increase in transportation cost, all transportation routes in the resource pool to be allocated are inserted into the remaining route set to obtain the optimization result. Here, the remaining route set is the route set generated after the set of routes to be optimized is removed from the set of routes to be optimized by M transportation routes.

[0102] In some embodiments of this application, after moving M transportation lines, including the target transportation line, from the same set of routes to be optimized into the resource pool to be allocated for each target transportation line, the device further includes: an election module, used to elect a center node in a center-radial mode for each set of remaining routes after the M transportation lines have been removed, and to move all transportation lines in the set of remaining routes that failed the election into the resource pool to be allocated if the election fails.

[0103] In some embodiments of this application, with the objective of minimizing the incremental transportation cost, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain an optimized result, including:

[0104] For each transportation route in the resource pool to be allocated, execute steps A1 to A2 until all routes are inserted into the remaining route set to obtain the optimization result. A1: Traverse each remaining route set. If the traversed remaining route set meets the target conditions, generate a random number within the target value range. If the random number is greater than a preset threshold, update the currently traversed remaining route set to the optimal insertion set. The target conditions include: after inserting the currently traversed remaining route set, a center node can be elected according to the center-radial model, and the increase in transportation cost is smaller; the preset threshold is within the target value range. A2: After traversing all remaining route sets, insert the currently targeted transportation route into the optimal insertion set. If the optimal insertion set is empty, insert the currently targeted transportation route into a newly created set of routes to be optimized.

[0105] In some embodiments of this application, with the objective of minimizing the incremental transportation cost, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain an optimized result, including:

[0106] With the goal of minimizing the incremental transportation cost, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain a new solution. Based on the annealing algorithm or hill climbing algorithm, it is determined whether to accept the new solution as the current solution. If the new solution is accepted as the current solution, the new solution is determined as the optimization result.

[0107] In some embodiments of this application, the apparatus further includes: an optimal solution module, configured to determine whether to accept the new solution as the optimal solution based on an annealing algorithm or a hill-climbing algorithm when the new solution is accepted as the current solution; and to update the optimal solution using the optimization results when the new solution is accepted as the optimal solution; correspondingly, the optimal result among multiple optimization results is the optimal solution in multiple optimization processes.

[0108] In some embodiments of this application, determining a target transportation route from multiple initial route sets includes: randomly selecting one transportation route from multiple initial route sets as the target route; selecting at least one transportation route whose distance from the target route is less than a target distance threshold; and using the selected target route and the at least one transportation route as the target transportation route.

[0109] In some embodiments of this application, the transportation route in the center-radial mode includes: a central node and radial nodes, wherein each radial node has a cargo transportation relationship with the central node.

[0110] It should be noted that the transportation route generation device provided in this embodiment belongs to the same inventive concept as the transportation route generation method provided in the above embodiments of this application specification. It can execute the transportation route generation method provided in any of the above embodiments of this specification, and specifically execute the corresponding functional modules and beneficial effects of the transportation route generation device. For technical details not described in detail in this embodiment, please refer to the specific processing content of the transportation route generation method provided in the above embodiments of the specification, which will not be repeated here.

[0111] Exemplary electronic devices

[0112] Another embodiment of this application also proposes an electronic device. Please refer to FIG11, which is a schematic diagram of the structure of the electronic device provided in the embodiment of this application. An exemplary embodiment of this specification also provides an electronic device, including: a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it performs the steps in the method for generating a transportation route according to various embodiments of this specification described in the above embodiments.

[0113] The internal structure of the electronic device is shown in Figure 11. The electronic device includes a processor, memory, network interface, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it follows the steps of the transportation route generation method according to various embodiments of this specification as described in the above embodiments.

[0114] The processor may include the main processor, as well as baseband chips, modems, etc.

[0115] The memory stores a computer program that executes the technical solution of this invention, and may also store an operating system and other key business functions. Specifically, the computer program may include program code, which includes computer operation instructions. More specifically, the memory may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0116] The processor can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0117] Input devices may include devices that receive data and information input by the user, such as keyboards, mice, cameras, scanners, light pens, voice input devices, touch screens, pedometers, or gravity sensors.

[0118] Output devices may include devices that allow information to be output to the user, such as displays, printers, speakers, etc.

[0119] The communication interface may include any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0120] The processor executes the computer program stored in the memory and calls other devices, which can be used to implement the various steps of any of the transportation route generation methods provided in the above embodiments of this application.

[0121] The electronic device may also include a display component and a voice component. The display component may be a liquid crystal display screen or an e-ink display screen. The input device of the electronic device may be a touch layer covering the display component, or a button, trackball or touchpad set on the casing of the electronic device, or an external keyboard, touchpad or mouse, etc.

[0122] Those skilled in the art will understand that the structure shown in Figure 11 is merely a block diagram of a portion of the structure related to the solution in this specification, and does not constitute a limitation on the electronic device to which the solution in this specification is applied. A specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0123] In addition to the methods and devices described above, the method for generating transportation routes provided in the embodiments of this specification can also be a computer program product, which includes a computer program that, when run by a processor, causes the processor to perform the steps in the method for generating transportation routes according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0124] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments described herein. Programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0125] Furthermore, embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of the steps in the method for generating a transport route according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0126] It is understood that the specific examples in this document are only intended to help those skilled in the art better understand the implementation methods of this specification, and are not intended to limit the scope of this specification.

[0127] It is understood that in the various embodiments described in this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments described in this specification.

[0128] It is understood that the various implementation methods described in this specification can be implemented individually or in combination, and the implementation methods in this specification are not limited in this respect.

[0129] Unless otherwise stated, all technical and scientific terms used in the embodiments of this specification have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this specification. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0130] It is understood that the processor in the embodiments of this specification can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this specification. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this specification can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0131] It is understood that the memory in the embodiments of this specification may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM). It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0132] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.

[0133] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the aforementioned method implementations, and will not be repeated here.

[0134] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0136] In addition, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0137] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of this specification, in essence, or the parts that contribute to the prior art, or parts of the technical solutions, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0138] The above description is merely a specific embodiment of this specification, but the scope of protection of this specification is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this specification should be included within the scope of protection of this specification. Therefore, the scope of protection of this specification should be determined by the scope of the claims.

Claims

1. A method for generating a transportation route, characterized in that, The method includes: Multiple initial route sets are generated based on multiple transport routes in the transport network; each initial route set includes at least one transport route. The multiple initial route sets are optimized multiple times according to the target optimization strategy to obtain multiple optimization results. Each subsequent optimization after the first optimization uses the previous optimization result as the route set to be optimized. The target optimization strategy includes: reallocating the target transportation route to the route set to be optimized, and creating new route sets to be optimized for target transportation routes that failed to be allocated. The target transportation route includes the target route and at least one transportation route whose distance to the target route is less than a target distance threshold. The target route is one of the multiple transportation routes, and the target route is different for each optimization. Based on the optimal result among the multiple optimization results, the transportation routes in the first type of route set are output as is, and the transportation routes in the second type of route set are adjusted to transportation routes in the center-radial mode and output.

2. The method according to claim 1, characterized in that, The first type of route set includes one transport route, and the second type of route set includes multiple transport routes.

3. The method according to claim 1 or 2, characterized in that, The initial set of routes is optimized multiple times according to the target optimization strategy, resulting in multiple optimization results, including: For each optimization, the target transportation route is determined from the plurality of initial route sets; For each target transportation route, M transportation routes that include the target transportation route and belong to the same set of routes to be optimized are moved into the resource pool to be allocated; wherein M is greater than or equal to 1, and the set of routes to be optimized is the optimization result of the previous optimization or the initial set of routes. With the goal of minimizing the incremental transportation cost, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain the optimization result. The set of remaining routes is the set of routes generated after the set of routes to be optimized is removed from the M transportation routes.

4. The method according to claim 1 or 2, characterized in that, For each target transportation route, after moving M transportation routes, including the target transportation route, from the same set of routes to be optimized into the resource pool to be allocated, the method further includes: For each set of remaining lines after the removal of the M transport lines, a center node is elected according to the center-radial model. If the election fails, all transport lines in the set of remaining lines that failed to be elected are moved into the resource pool to be allocated.

5. The method according to claim 1 or 2, characterized in that, With the objective of minimizing incremental transportation costs, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain the optimization result, including: Steps A1 to A2 are executed for each transportation route in the resource pool to be allocated until all routes are inserted into the remaining route set to obtain the optimization result; A1. Traverse each set of remaining routes. If the traversed set of remaining routes meets the target conditions, generate a random number within the target value range. If the random number is greater than a preset threshold, update the currently traversed set of remaining routes to the optimal insertion set. The target conditions include: after inserting into the currently traversed set of remaining routes, a central node can be elected according to the center-radial model, and the increase in transportation cost is smaller; the preset threshold is within the target value range. A2, after traversing all remaining route sets, inserts the currently targeted transportation route into the optimal insertion set, and if the optimal insertion set is empty, inserts the currently targeted transportation route into the newly created set of routes to be optimized.

6. The method according to claim 1 or 2, characterized in that, With the objective of minimizing incremental transportation costs, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain the optimization result, including: With the goal of minimizing the incremental transportation cost, all transportation routes in the resource pool to be allocated are inserted into the set of remaining routes to obtain a new solution; Based on the annealing algorithm or hill climbing algorithm, determine whether to accept the new solution as the current solution, and if the new solution is accepted as the current solution, determine the new solution as the optimization result.

7. The method according to claim 6, characterized in that, The method further includes: if the new solution is accepted as the current solution, determining whether to accept the new solution as the optimal solution based on the annealing algorithm or the hill climbing algorithm; If the new solution is accepted as the optimal solution, the optimal solution is updated using the optimization results; Accordingly, the optimal result among the multiple optimization results is the optimal solution in the multiple optimization processes.

8. The method according to claim 1 or 2, characterized in that, Determining the target transport route from the plurality of initial route sets includes: Randomly select one of the multiple initial route sets as the target route; Select at least one transport route whose distance from the target route is less than the target distance threshold; Select the target route and at least one transport route as the target transport route.

9. The method according to any one of claims 1 to 8, characterized in that, The transportation route in the center-radial mode includes a central node and radial nodes, wherein each radial node has a cargo transportation relationship with the central node.

10. The method according to any one of claims 1 to 7, characterized in that, Output the transportation routes in the first type of route set as is, and adjust the transportation routes in the second type of route set to the center-radial mode and output them, including: The original state of the transportation routes in the first type of route set is output through the display device, and the transportation routes in the second type of route set are adjusted into transportation routes in the center-radial mode and output through the display device.

11. An electronic device, characterized in that, include: Processor and memory; The memory is connected to the processor and is used to store computer programs; The processor is configured to implement the method for generating transport routes as described in any one of claims 1 to 10 by running a computer program stored in the memory.

12. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method for generating transportation routes as described in any one of claims 1 to 10.

13. A computer program product, characterized in that, The computer program product stores a computer program, which, when executed by a processor, implements the method for generating transportation routes as described in any one of claims 1 to 10.

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