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 central logistics node and a plurality of service logistics nodes of a logistics network, generating initial transport routes, wherein each service logistics node has a cargo transportation relationship with the central logistics node; according to a target optimization strategy, performing a plurality of optimizations on the initial transport routes, so as to determine a plurality of optimization results, wherein the target optimization strategy comprises reconstructing a transport route corresponding to a target service logistics node, and the target service logistics node comprises a target node and at least one service logistics node, the distance from which to the target node is less than a target distance threshold value; and determining an optimization result having the minimum transportation cost among the plurality of optimization results as a target transport route of the logistics network. 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] Logistics networks typically radiate outwards from distribution centers, forming a radial layout. The process of transporting goods collected from service points to distribution centers is called consolidation. Conversely, the process of transporting goods from distribution centers to various service points is called distribution freight. Establishing a separate transportation route between each service point and distribution center would result in an excessive number of routes, leading to a significant increase in operating costs.

[0003] Therefore, a typical route needs to pass through multiple service points. For example, in a consolidated cargo scenario, each vehicle collects goods from multiple service points and then delivers them to a distribution center. In a loose cargo scenario, each vehicle loads goods from multiple points at the distribution center and then transports the goods to the service points in a specific order. Currently, logistics network transportation routes can only be designed by planners relying on experience.

[0004] However, the above-mentioned method of manually setting up transportation routes (manual route planning) is not only too time-consuming, but the results of the route planning are often unreliable, resulting in low vehicle utilization. Summary of the Invention

[0005] 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.

[0006] The first aspect of this application provides a method for generating a transportation route. The method includes: generating an initial transportation route based on a central logistics node and multiple service logistics nodes of a logistics network; wherein each service logistics node has a cargo transportation relationship with the central logistics node; optimizing the initial transportation route multiple times according to a target optimization strategy to determine multiple optimization results; wherein each optimization after the first optimization determines the previous optimization result as the transportation route to be optimized; the target optimization strategy includes: reconstructing the transportation route corresponding to a target service logistics node; the target service logistics node includes a target node and at least one service logistics node whose distance to the target node is less than a target distance threshold; the target node is one of multiple service logistics nodes, and the target node is different for each optimization; determining the optimization result with the best transportation cost among the multiple optimization results as the target transportation route of the logistics network.

[0007] Optionally, the initial transportation route is optimized multiple times according to the target optimization strategy to determine multiple optimization results, including: for each optimization, determining the target service logistics node from multiple service logistics nodes; for each target service logistics node, removing M consecutive service logistics nodes, including the target service logistics node, from the transportation route to be optimized; wherein M is greater than or equal to 1, and the transportation route to be optimized is the optimization result of the previous optimization or the initial transportation route; with the goal of minimizing the increase in transportation cost, inserting the M removed service logistics nodes into the remaining transportation route to obtain the optimization result, wherein the remaining transportation route is the transportation route remaining after removing M service logistics nodes from the transportation route to be optimized.

[0008] Optionally, with the goal of minimizing the incremental transportation cost, the M dismantled service logistics nodes are inserted into the remaining transportation lines to obtain the optimization result. This includes: executing steps A1 to A2 for each of the M dismantled service logistics nodes until all are inserted into the remaining transportation lines to obtain the optimization result; A1, traversing each insertion position in each segment of the remaining transportation lines, generating a random number within the target value range when the traversed insertion position meets the target conditions, and updating the currently traversed insertion position to the optimal insertion position when the random number is greater than a preset threshold; wherein, the target conditions include: after insertion at the currently traversed insertion position, the business constraints of the logistics network are satisfied; the preset threshold is within the target value range; A2, after traversing all insertion positions in all transportation lines, inserting the currently targeted service logistics node into the optimal insertion position.

[0009] Optionally, after traversing all insertion positions in all transportation routes, the method further includes: if the optimal insertion position is empty, establishing a direct transportation route between the currently targeted service logistics node and the central logistics node in the remaining transportation routes.

[0010] Optionally, with the goal of minimizing the incremental transportation cost, the M service logistics nodes to be removed are inserted into the remaining transportation route to obtain the optimization result, including: with the goal of minimizing the incremental transportation cost, the M service logistics nodes to be removed are re-inserted into the remaining transportation route to obtain a new solution; based on the simulated 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 a simulated 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; and determining the optimization result with the best transportation cost among multiple optimization results as the target transportation route of the logistics network, including: determining the optimal solution in multiple optimization processes as the target transportation route of the logistics network.

[0012] Optionally, determining the target service logistics node from multiple service logistics nodes includes: randomly selecting one of the multiple service logistics nodes as the target node; selecting at least one service logistics node whose distance from the target node is less than a target distance threshold; and using the selected target node and at least one service logistics node as the target service logistics node.

[0013] Optionally, the initial transportation routes include: direct transportation routes between each service logistics node and the central logistics node.

[0014] Optionally, after determining the optimal transportation route with the best transportation cost among multiple optimization results as the target transportation route of the logistics network, the method further includes: displaying the target transportation route through a display device.

[0015] Optionally, reconstructing the transportation route corresponding to the target service logistics node includes: dismantling the transportation route corresponding to the target service logistics node and re-inserting the dismantled service logistics node into the transportation route of the logistics network.

[0016] 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.

[0017] 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.

[0018] A fourth aspect of this application provides a computer program product, including: a computer program, and a method for generating a transport route according to the first aspect when the computer program is executed by a processor.

[0019] As can be seen from the above technical solution, the embodiments of this application can first automatically generate the initial transportation route of the logistics network, and then continuously optimize the initial transportation route through multiple optimizations to finally obtain the optimal optimization result, i.e., the target transportation route of the logistics network. Compared with manual route planning, it can handle larger and more complex logistics networks, complete route planning more quickly, and output the target transportation route as the final result. At the same time, it can avoid the arbitrariness and subjectivity of manual route planning, making the effect of the route planning result more reliable. Furthermore, each optimization will reconstruct the transportation routes corresponding to each target service logistics node with relatively concentrated geographical locations, thereby obtaining the optimal optimization result faster and reducing the number of optimizations. Attached Figure Description

[0020] 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.

[0021] Figure 1 is one of the flowcharts of a method for generating a transportation route according to an embodiment of this application.

[0022] Figure 2 is a schematic diagram of the logistics network structure in an embodiment of this application;

[0023] Figure 3 is one of the schematic diagrams of dismantling service logistics nodes in the embodiments of this application.

[0024] Figure 4 is a second flowchart of a method for generating a transportation route according to an embodiment of this application.

[0025] Figure 5 is a second schematic diagram of dismantling service logistics nodes in an embodiment of this application.

[0026] Figure 6 is one of the schematic diagrams of inserting service logistics nodes in the embodiments of this application.

[0027] Figure 7 is a second schematic diagram of inserting service logistics nodes in an embodiment of this application.

[0028] Figure 8 is a schematic diagram of the structure of a transportation route generation device provided in an embodiment of this application.

[0029] Figure 9 is a schematic diagram of the electronic device structure provided in an embodiment of this application. Detailed Implementation

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Overview

[0034] As described in the background section, manual route planning has several drawbacks. 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 time delays. After the route planning results are put into operation, planners find it difficult to respond and make adjustments in a timely manner to meet fluctuations in demand and network expansion, resulting in low utilization rates for some vehicles.

[0035] 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.

[0036] To address the aforementioned problems, the inventors of this application propose that during the automatic route planning process using computer equipment, when optimizing the initial solution or initial transportation route, optimization can be purposefully performed in conjunction with the actual situation of the logistics network. Therefore, this application provides a method for generating transportation routes, aiming to automatically generate the initial transportation route of the logistics network first, and then continuously optimize the initial transportation route through multiple optimizations to ultimately obtain the optimal optimization result, i.e., the target transportation route of the logistics network. Compared to manual route planning, this method can handle larger and more complex logistics networks, complete route planning more quickly, and output the target transportation route as the final result. Simultaneously, it avoids the arbitrariness and subjectivity of manual route planning, ensuring a more reliable effect. Furthermore, during each optimization, the transportation routes corresponding to each target service logistics node with a relatively concentrated geographical location are reconstructed, thereby obtaining the optimal optimization result faster and reducing the number of optimizations. The specific solution is described in detail below through embodiments.

[0037] Exemplary methods

[0038] This application provides an exemplary description of a method for generating transportation routes, as shown in Figure 1. The method for generating transportation routes includes:

[0039] S101: Generate the initial transportation route based on the central logistics node and multiple service logistics nodes of the logistics network.

[0040] It should be noted that 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 multiple logistics nodes in a designated business area. These multiple logistics nodes may include a central logistics node and multiple service logistics nodes. A central logistics node may be, for example, a distribution center or a transshipment center. Service logistics nodes may be, for example, the origin of goods in a consolidated cargo scenario or the destination of goods in a bulk cargo scenario.

[0041] It is worth noting that each service logistics node in the logistics network has a cargo transportation relationship with the central logistics node. In a consolidated cargo scenario, the goods of each service logistics node need to be transported to the central logistics node. In a loose cargo scenario, the goods of the central logistics node need to be transported to each service logistics node. The initial transportation routes include transportation routes from each service logistics node to the central logistics node, or transportation routes from the central logistics node to each service logistics node.

[0042] 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, 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 capacity 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.

[0043] In some embodiments, the initial transport route includes a direct transport route between each service logistics node and the central logistics node.

[0044] S102: Optimize the initial transportation route multiple times according to the target optimization strategy and determine multiple optimization results.

[0045] It should be noted that during the initial optimization, the initial transportation route is used as the route to be optimized. Each subsequent optimization uses the result of the previous optimization as the route to be optimized. 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 transportation route. By continuously trying to find the optimal transportation route, an optimal result is obtained as the optimal transportation route. 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: reconstructing the transportation routes corresponding to the target service logistics nodes; the target service logistics nodes include the target node and at least one service logistics node whose distance to the target node is less than the target distance threshold; the target node is one of multiple service logistics nodes, and the target node is different for each optimization. It can be understood that the initial transportation route or the transportation route to be optimized consists of a central logistics node and service logistics nodes. The target service logistics node, as a part of the logistics nodes, corresponds to a transportation route that, for example, includes itself. As shown in Figure 2, the logistics network consists of 13 logistics nodes: a, b, c, d, e, f, g, h, i, j, k, l, and m, where m is the central logistics node and the rest are service logistics nodes. Assuming a, b, and c are the selected target service logistics nodes, then the transportation route corresponding to a can be bad, ibad, or badk, as long as the transportation route includes a. Similarly, the transportation route corresponding to c can be ecf or hecf, as long as the transportation route includes c.

[0047] It's worth noting that the target distance threshold can be a pre-set threshold. Here, situations where the distance between service logistics nodes is less than the target distance threshold can be considered as cases where the service logistics nodes are close together or concentrated. Therefore, in each optimization, selecting the transportation routes corresponding to some concentrated service logistics nodes as the optimization direction can quickly approach the optimal result and reduce the number of optimization attempts.

[0048] S103: Determine the optimal transportation route for the logistics network from the multiple optimization results that have the best transportation cost.

[0049] It should be noted that each optimization result is an attempt to find the optimal transportation route, and it represents one possible transportation route. 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 lowest transportation cost. This result is then taken as the optimal transportation route, i.e., the target transportation route. 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 selected as the target transportation route.

[0050] In some embodiments, the optimization results include not only the relationships between service logistics nodes and between service logistics nodes and the central logistics node on the line, but also data such as departure time, departure frequency, number of departures, and vehicle type.

[0051] In some embodiments, after the target transportation route is determined, it can be directly output. For example, the target transportation route can be displayed on a display device for personnel to view.

[0052] In this embodiment, an initial transportation route for the logistics network can be automatically generated first, and then continuously optimized through multiple optimizations. The final optimal optimization result, i.e., the target transportation route of the logistics network, is obtained. Compared to manual route planning, this method can handle larger and more complex logistics networks, complete route planning more quickly, and output the target transportation route as the final result. Simultaneously, it avoids the arbitrariness and subjectivity of manual route planning, ensuring a more reliable outcome. Furthermore, during each optimization, the transportation routes corresponding to the geographically concentrated target service logistics nodes are reconstructed, 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 used as an example for illustration. In this embodiment, the initial transportation route is optimized multiple times according to the target optimization strategy, determining multiple optimization results, including:

[0054] For each optimization, the target service logistics node is determined from multiple service logistics nodes;

[0055] For each target service logistics node, remove M consecutive service logistics nodes, including the target service logistics node, from the transportation route to be optimized; where M is greater than or equal to 1, and the transportation route to be optimized is the optimization result of the previous optimization or the initial transportation route.

[0056] With the goal of minimizing the incremental transportation cost, the M service logistics nodes to be removed are inserted into the remaining transportation route to obtain the optimization result. The remaining transportation route is the transportation route generated after removing the M service logistics nodes from the transportation route to be optimized.

[0057] It should be noted that the relevant descriptions of the above embodiments regarding the target service logistics node are provided and will not be repeated here. Furthermore, this application does not limit the method for determining the target service logistics node from multiple service logistics nodes. For example, the geographical location information of each service logistics node can be obtained, and then, based on this geographical location information, service logistics nodes that meet the requirements can be selected as the target service logistics node.

[0058] It is understandable that reconstructing the transportation route corresponding to the target service logistics node includes two steps: dismantling and reconstruction. For the dismantling step, dismantling the transportation route corresponding to the target service logistics node can also be considered as dismantling the target service logistics node or, together with other service logistics nodes continuous on the transportation route. Continuing with Figure 2 as an example, for the target service logistics node a, its corresponding transportation route can be bad. Therefore, dismantling the transportation route corresponding to the target service logistics node can also be considered as dismantling b and d, which are on the same transportation route as a, along with a. The logistics network after dismantling can be shown in Figure 3. From Figure 3, it can be seen that a, b, and d are in an isolated state and are not on any transportation route. It is worth noting that the value of M can be freely set based on experience; for example, M can be 3. In some embodiments, in the dismantling step, for each target service logistics node, M non-contiguous service logistics nodes, including the target service logistics node, in the transportation route to be optimized can also be dismantled.

[0059] The reconstruction step involves re-inserting the dismantled service logistics nodes into the transportation routes of the logistics network. For example, in the remaining transportation routes obtained after dismantling, as shown in Figure 3, nodes a, b, and d, which are currently isolated, are not included. In the reconstruction step, nodes a, b, and d need to be reinserted so that there are no isolated service logistics nodes in the re-inserted logistics network. It is worth noting that the optimization objective of this application is to find the transportation route with the optimal transportation cost; therefore, the incremental transportation cost needs to be considered when inserting service logistics nodes. The reconstruction step is performed with the objective of minimizing the incremental transportation cost.

[0060] In this embodiment, when dismantling a transportation route or service logistics node, M service logistics nodes on the same segment of the transportation route, including the target service logistics node, can be dismantled. By reasonably controlling the value of M, the optimization time can be shortened while avoiding damage to the optimized transportation route.

[0061] In some embodiments of this application, in this embodiment, with the objective of minimizing the incremental transportation cost, the M service logistics nodes to be removed are inserted into the remaining transportation route to obtain the optimized result, including:

[0062] For each of the M dismantled service logistics nodes, execute steps A1 to A2 until all are inserted into the remaining transportation routes to obtain the optimized result;

[0063] A1. Traverse each insertion position in each segment of the remaining transportation routes. If the traversed insertion position 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 insertion position to the optimal insertion position. The target conditions include: after inserting at the currently traversed insertion position, the business constraints of the logistics network are met, and the increase in transportation costs is smaller; the preset threshold is within the target value range.

[0064] A2, after traversing all insertion positions in all transportation routes, inserts the currently targeted service logistics node into the optimal insertion position.

[0065] It should be noted that the process of inserting each service logistics node into the remaining transportation route is the same. To reduce repetition, only one insertion process will be used as an example here. There are usually multiple insertion positions in the remaining transportation route. Any two adjacent service logistics nodes in the same transportation route can be regarded as an insertion position. For example, in Figure 3, the positions between j and i, i and k, k and l, l and m, and g and h are all insertion positions.

[0066] This embodiment aims to minimize the incremental transportation cost by finding the most suitable insertion position, i.e., the optimal insertion position. To avoid getting trapped in local optima, this embodiment sets a suboptimal insertion criterion, which has a threshold p. If a feasible insertion position is found, it is skipped with probability p. This method can skip better insertion positions with a certain probability and select a suboptimal insertion position, thereby avoiding getting trapped in local optima. For example, in this embodiment, if the traversed insertion position meets the target conditions, it is considered a feasible insertion position. If this insertion position is directly considered the optimal insertion position and updated, it may cause the problem of local optima. In this case, a random number r within the target value range is generated, and the value of r is compared with the preset threshold p. Only if r is greater than p is the insertion position considered the optimal insertion position and updated.

[0067] In some embodiments, for any service logistics node z that is removed, the direct transportation cost of z (the cost of direct transportation between z and the central logistics node) can first be calculated, denoted as m. Then, initialize by setting the optimal transportation cost increment d* = m, the optimal insertion route t* as null, and the optimal insertion position i* as null. Iterate through t∈T (T is the set of all routes in the remaining transportation routes) and i∈P_t (P_t is the set of possible insertion positions for route t). If z can be inserted into position i of route t (i.e., it satisfies all constraints after insertion), then perform the following steps:

[0068] Calculate the incremental transportation cost d after insertion.

[0069] If at this time t* is a null value or d < d*, a more optimal insertable position is considered to be found. A random number r between 0 and 1 is generated. If r is greater than p, let d* = d, t* = t, and i* = i.

[0070] Finally, according to the values of t* and i*, z is inserted into the optimal insertable position.

[0071] In the embodiment of the present application, a threshold is set. If a feasible insertable position is found, the insertable position is skipped by this threshold, so that a relatively optimal insertable position can be skipped with a certain probability, and a sub-optimal insertable position is selected, thereby avoiding falling into a local optimal solution.

[0072] To avoid generating isolated service logistics nodes in the case of no optimal insertable position, after traversing all the insertable positions in the transportation lines, the method further includes:

[0073] In the case where the optimal insertable position is empty, a direct transportation line between the currently targeted service logistics node and the central logistics node is established in the remaining transportation lines.

[0074] It should be noted that the empty optimal insertable position indicates that the corresponding service logistics node cannot be inserted into any section of the remaining transportation lines. At this time, the insertion cost will be higher than the direct transportation cost. Therefore, a direct transportation line between this service logistics node and the central logistics node can be directly established.

[0075] In the embodiment of the present application, isolated service logistics nodes can be avoided in the case of no optimal insertable position.

[0076] In some embodiments of the present application, with the goal of minimizing the increment of transportation cost, the M removed service logistics nodes are inserted into the remaining transportation lines to obtain an optimization result, including:

[0077] With the goal of minimizing the increment of transportation cost, the M removed service logistics nodes are reinserted into the remaining transportation lines to obtain a new solution; based on the simulated annealing algorithm or the hill climbing algorithm, it is determined whether to accept the new solution as the current solution, and in the case of accepting the new solution as the current solution, the new solution is determined as the optimization result.

[0078] It should be noted that multiple optimizations of the initial transportation line according to the target optimization strategy can be regarded as a process of continuously solving the optimal solution according to the specified algorithm. Each obtained optimization result is a solution. And the optimal solution is the optimal optimization result or transportation line of the transportation cost.

[0079] 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-range 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. The Simulated Annealing algorithm is an optimization algorithm with a serial structure that endows the search process with a time-varying and finally zero-tending probability jump property, thus effectively avoiding falling into local minima and finally tending to the global optimal.

[0080] 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 the 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.

[0081] 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.

[0082] 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 Simulated 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, using the optimization result to update the optimal solution;

[0083] Determining the optimization result with the optimal transportation cost among multiple optimization results as the target transportation route of the logistics network includes: determining the optimal solution in multiple optimization processes as the target transportation route of the logistics network.

[0084] 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 target transportation route of the logistics network. 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 temperature reduction operation is also required, that is, let T = T × t. Here, T represents the current temperature, and t represents the cooling factor.

[0085] 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.

[0086] In some embodiments of this application, determining a target service logistics node from a plurality of service logistics nodes includes:

[0087] Randomly select one of multiple service logistics nodes as the target node; select at least one service logistics node whose distance from the target node is less than the target distance threshold; use the selected target node and at least one service logistics node as the target service logistics node.

[0088] It should be noted that during each optimization process, different objects can be selected as target service logistics nodes from multiple service logistics nodes. In this embodiment, the selection of the first target service logistics node (target node) is initiated by random selection. After determining the target node, service logistics nodes that meet the distance requirements can be easily identified using the geographical location information between each service logistics node. These service logistics nodes that meet the distance requirements, along with the target node, are then used as target service logistics nodes.

[0089] In this embodiment of the application, by randomly selecting the target node, different objects can be selected as the target service logistics node from multiple service logistics nodes in each optimization process.

[0090] To facilitate understanding, the following example of a cargo consolidation scenario will be used to illustrate the method for generating transportation routes provided in this application.

[0091] As shown in Figure 4, it includes:

[0092] S401: Collect input information. This input information is the business information of the logistics network. This business information includes, but is not limited to, 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 after its earliest available departure time, the time of cargo arrival at the destination before its latest arrival time, and the total weight of cargo served by each transport route not exceeding the capacity of the vehicle type.

[0093] S402: Construct the initial solution. This initial solution is equivalent to the initial transportation route in the above embodiments. For example, each cargo point can be abstracted as a service point. The location of this service point is where the cargo needs to be loaded (in the consolidation scenario) or unloaded (in the bulk cargo scenario). Establish a service point set N, which is equivalent to the service logistics node in the above embodiments. Traverse a∈N and arrange a direct transportation route for each service point a. In the consolidation scenario, the starting point of the transportation route is a, and the ending point is the distribution center, with no other service points in between; in the bulk cargo scenario, the starting point of the transportation route is the distribution center, and the ending point is a, with no other service points in between. Initialize the resource pool S to be allocated as an empty set. Set two variables: the current solution sc and the optimal solution s*. Initialize the simulated annealing temperature T and the cooling factor t.

[0094] S403: Destroy the line. For example, a service point a can be randomly selected. Find the set of service points adjacent to service point a (including a itself), denoted as Z. Traverse z∈Z. If z is already in the resource pool S to be allocated, skip z. Find the transport line t where z is located. Randomly select a segment of length L from transport line t that contains z. Remove the selected segment of service points from transport line t and put these service points into the resource pool to be allocated. If the number of service points in the resource pool S to be allocated has reached the upper limit of the number of service points destroyed, then terminate. As shown in Figure 2, assume that the randomly selected service point is a in Figure 2, and the set Z is {a,b,c}. Then, firstly, for a in set Z, select multiple service points from the transport line where a is located. For example, the selected service points can be a, b, and d. After removing a, b, and d, it is shown in Figure 3. Then, for b in set Z, whose transportation line has been destroyed, we continue to select multiple service points from the transportation line where c is located, such as e and c. We then dismantle e and c, as shown in Figure 5. All dismantled service points are added to the resource pool S to be allocated.

[0095] S404: Reconstruct the routes. For example, reinsert the service points in the to-be-allocated resource pool S into the routes to construct a new feasible solution. The basis for determining which route to insert the service point 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 insertion position with the minimum cost increment is the optimal insertable position. In particular, if a service point cannot be inserted into any route, or the minimum cost increment is higher than the direct shipping cost, then arrange a direct shipping route for the service point. Specifically, the service points in the to-be-allocated resource pool S can be sorted. Traverse z ∈ S. First, calculate the direct shipping cost of z (the cost of direct shipping between z and the central logistics node), denoted as m. Then initialize, let the optimal transportation cost increment d* = m, the optimal inserted route t* be null, and the optimal insertion position i* be null. Traverse t ∈ T (T is the set of all remaining routes) and i ∈ P_t (P_t is the set of insertable positions on route t). If z can be inserted into position i of route t (i.e., all constraints can be satisfied after insertion), then perform the following steps:

[0096] Calculate the increment of transportation cost d after insertion.

[0097] If t* is null or d < d* at this time, it is considered that a more optimal insertable position is found. Generate a random number r between 0 and 1. If r is greater than p, let d* = d, t* = t, and i* = i.

[0098] Finally, if there is no optimal insertable position, arrange a direct shipping route for z. Otherwise, insert z into the optimal insertable position according to the values of t* and i*.

[0099] If the transportation routes after removing the service points are as shown in Figure 5, for service point d, if its optimal insertable position is between positions i and k, then after inserting d, it can be as shown in Figure 6. Correspondingly, after inserting all the removed service points, it can be as shown in Figure 7.

[0100] S405: Determine whether to accept the new solution as the current solution. If so, execute S406; if not, execute S409. 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.

[0101] S406: Update the current solution and update the current solution to the new solution.

[0102] S407: Determine whether to accept the new solution as the optimal solution. If so, execute S408; if not, execute S409. 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.

[0103] S408: Update the optimal solution and update the optimal solution to the new solution.

[0104] S409: Determine if the termination condition is met. If yes, proceed to S410; otherwise, proceed to S403. The 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).

[0105] S410: Output the optimal solution.

[0106] 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 the given constraints and does not rely on human subjective judgment, it avoids the arbitrariness and subjectivity of manual planning. Finally, through computer computation, hundreds or even thousands of transportation routes can be planned within minutes, making it more efficient.

[0107] Exemplary device

[0108] In this embodiment of the application, a transportation route generation apparatus is also provided, as shown in FIG8. The transportation route generation apparatus includes:

[0109] The first generation module 801 is used to generate an initial transportation route based on the central logistics node and multiple service logistics nodes of the logistics network; wherein each service logistics node has a cargo transportation relationship with the central logistics node.

[0110] The optimization module 802 is used to optimize the initial transportation route multiple times according to the target optimization strategy and determine multiple optimization results. Each subsequent optimization after the first optimization uses the previous optimization result as the transportation route to be optimized. The target optimization strategy includes: reconstructing the transportation route corresponding to the target service logistics node; the target service logistics node includes the target node and at least one service logistics node whose distance to the target node is less than a target distance threshold; the target node is one of multiple service logistics nodes, and the target node is different for each optimization.

[0111] The second generation module 803 is used to determine the optimal transportation cost among multiple optimization results as the target transportation route of the logistics network.

[0112] In some embodiments of this application, the optimization module 802 performs multiple optimizations on the initial transportation route according to the target optimization strategy, determining multiple optimization results, including:

[0113] For each optimization, the target service logistics node is determined from multiple service logistics nodes;

[0114] For each target service logistics node, remove M consecutive service logistics nodes, including the target service logistics node, from the transportation route to be optimized; where M is greater than or equal to 1, and the transportation route to be optimized is the optimization result of the previous optimization or the initial transportation route.

[0115] With the goal of minimizing the incremental transportation cost, the M service logistics nodes to be removed are inserted into the remaining transportation route to obtain the optimization result. The remaining transportation route is the transportation route generated after removing the M service logistics nodes from the transportation route to be optimized.

[0116] In some embodiments of this application, with the objective of minimizing the incremental transportation cost, the M service logistics nodes to be removed are inserted into the remaining transportation route to obtain an optimized result, including:

[0117] For each of the M dismantled service logistics nodes, execute steps A1 to A2 until all are inserted into the remaining transportation routes to obtain the optimized result;

[0118] A1. Traverse each insertion position in each segment of the remaining transportation routes. If the traversed insertion position 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 insertion position to the optimal insertion position. The target conditions include: after inserting at the currently traversed insertion position, the business constraints of the logistics network are met, and the increase in transportation costs is smaller; the preset threshold is within the target value range.

[0119] A2, after traversing all insertion positions in all transportation routes, inserts the currently targeted service logistics node into the optimal insertion position.

[0120] In some embodiments of this application, the apparatus further includes a direct delivery module, configured to: establish a direct delivery route between the currently targeted service logistics node and the central logistics node in the remaining transportation routes when the optimal insertion position is empty.

[0121] In some embodiments of this application, with the goal of minimizing the incremental transportation cost, the M service logistics nodes to be removed are inserted into the remaining transportation route to obtain an optimization result. This includes: with the goal of minimizing the incremental transportation cost, the M service logistics nodes to be removed are re-inserted into the remaining transportation route to obtain a new solution; based on the simulated 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.

[0122] In some embodiments of this application, the device further includes: an acceptance judgment module, configured to, if a new solution is accepted as the current solution, determine whether to accept the new solution as the optimal solution based on a simulated annealing algorithm or a hill-climbing algorithm; and if the new solution is accepted as the optimal solution, update the optimal solution using the optimization results;

[0123] The optimal solution with the best transportation cost among multiple optimization results is determined as the target transportation route of the logistics network, including determining the optimal solution in multiple optimization processes as the target transportation route of the logistics network.

[0124] In some embodiments of this application, determining a target service logistics node from multiple service logistics nodes includes: randomly selecting one of the multiple service logistics nodes as the target node; selecting at least one service logistics node whose distance from the target node is less than a target distance threshold; and using the selected target node and at least one service logistics node as the target service logistics node.

[0125] In some embodiments of this application, the initial transport route includes: a direct transport route between each service logistics node and the central logistics node.

[0126] It should be noted that the transportation route generation device provided in this embodiment belongs to the same application 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. Here, it will not be repeated.

[0127] Exemplary electronic devices

[0128] Another embodiment of this application also proposes an electronic device. Please refer to FIG9, 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 the processor executes the computer program to perform the steps in the method for generating a transportation route according to various embodiments of this specification described in the above embodiments.

[0129] The internal structure of the electronic device is shown in Figure 9. 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 the 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.

[0130] The processor may include the main processor, as well as baseband chips, modems, etc.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Output devices may include devices that allow information to be output to the user, such as displays, printers, speakers, etc.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Those skilled in the art will understand that the structure shown in Figure 9 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.

[0139] 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.

[0140] The computer program product described herein can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments described herein. These 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 be executed entirely on the user's computing device, partially on the user's computing 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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: An initial transportation route is generated based on a central logistics node and multiple service logistics nodes in a logistics network; wherein each of the service logistics nodes has a cargo transportation relationship with the central logistics node. The initial transportation route is optimized multiple times according to the target optimization strategy to determine multiple optimization results. Each subsequent optimization after the first optimization uses the previous optimization result as the transportation route to be optimized. The target optimization strategy includes: reconstructing the transportation route corresponding to the target service logistics node. The target service logistics node includes the target node and at least one service logistics node whose distance to the target node is less than a target distance threshold. The target node is one of the multiple service logistics nodes, and the target node is different for each optimization. The optimization result with the best transportation cost among the multiple optimization results is determined as the target transportation route of the logistics network.

2. The method according to claim 1, characterized in that, The initial transportation route was optimized multiple times according to the target optimization strategy, resulting in several optimization outcomes, including: For each optimization, the target service logistics node is determined from the plurality of service logistics nodes; For each target service logistics node, M consecutive service logistics nodes, including the target service logistics node, in the transportation route to be optimized are removed; where M is greater than or equal to 1, and the transportation route to be optimized is the optimization result of the previous optimization or the initial transportation route. With the goal of minimizing the increase in transportation costs, the M service logistics nodes to be removed are inserted into the remaining transportation routes to obtain the optimization result. The remaining transportation routes are the transportation routes remaining after the M service logistics nodes are removed from the transportation routes to be optimized.

3. The method according to claim 2, characterized in that, With the objective of minimizing incremental transportation costs, the M service logistics nodes to be dismantled are inserted into the remaining transportation routes to obtain optimized results, including: For each of the M service logistics nodes that have been dismantled, execute steps A1 to A2 respectively until all are inserted into the remaining transportation lines to obtain the optimized result; A1. Traverse each insertion position in each segment of the remaining transportation routes. If the traversed insertion position 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 insertion position as the optimal insertion position. The target conditions include: after inserting at the currently traversed insertion position, the business constraints of the logistics network are satisfied, and the increase in transportation costs is smaller; the preset threshold is within the target value range. A2, after traversing all insertion positions in all transportation routes, inserts the currently targeted service logistics node into the optimal insertion position.

4. The method according to claim 3, characterized in that, After traversing all insertion positions in all transport routes, the method further includes: If the optimal insertion position is empty, a direct transportation route between the currently targeted service logistics node and the central logistics node is established in the remaining transportation routes.

5. The method according to claim 2, characterized in that, With the objective of minimizing incremental transportation costs, the M service logistics nodes to be dismantled are inserted into the remaining transportation routes to obtain optimized results, including: With the goal of minimizing the increase in transportation costs, the M service logistics nodes that were removed are re-inserted into the remaining transportation routes to obtain a new solution; Based on the simulated 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.

6. The method according to claim 5, 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 simulated 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; Among these optimization results, the optimization result with the best transportation cost is determined as the target transportation route of the logistics network, including: The optimal solution obtained from multiple optimization processes is determined as the target transportation route of the logistics network.

7. The method according to claim 2, characterized in that, Determining the target service logistics node from the plurality of service logistics nodes includes: Randomly select one of the plurality of service logistics nodes as the target node; Select at least one service logistics node whose distance from the target node is less than the target distance threshold; The selected target node and at least one service logistics node are designated as the target service logistics node.

8. The method according to any one of claims 1 to 7, characterized in that, The initial transportation route includes: a direct transportation route between each service logistics node and the central logistics node.

9. The method according to any one of claims 1 to 7, characterized in that, After determining the optimization result with the optimal transportation cost among the multiple optimization results as the target transportation route of the logistics network, the process further includes: The target transportation route is displayed on a display device.

10. The method according to any one of claims 1 to 7, characterized in that, The reconstruction of the transportation route corresponding to the target service logistics node includes: dismantling the transportation route corresponding to the target service logistics node and re-inserting the dismantled service logistics node into the transportation route of the logistics network.

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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