Regional delivery volume optimization method, device, computer equipment and storage medium

Abstract

The present application relates to a method, apparatus, computer device, and storage medium for optimizing the regional delivery volume. By obtaining a request for optimizing the regional delivery volume, the timeout area corresponding to the request for optimizing the regional delivery volume is determined; the non-timeout areas adjacent to the timeout area are obtained, and delivery optimization operations are performed on the non-timeout areas to obtain the time benefits corresponding to the delivery optimization operations; according to the time benefits, a weighted bipartite graph maximum matching problem is constructed; the weighted bipartite graph maximum matching problem is solved to obtain the allocation result corresponding to the weighted bipartite graph maximum matching problem; and the regional delivery volume is optimized according to the allocation result. The present application determines the timeout area and non-timeout area corresponding to the delivery, and then performs delivery optimization and delivery allocation based on the benefits. Through the dynamic programming of the delivery, the delivery transport capacity is more reasonably allocated, and the processing efficiency of the delivery process is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to an optimization method, device, computer equipment, and storage medium for regional delivery volume. Background Art

[0002] With the development of Internet technology, online shopping has become a lifestyle for many people. The logistics industry has also developed rapidly. In the delivery link of modern logistics scenarios, delivery outlets are usually configured in various blocks of the city, and deliverymen then use light vehicles (such as electric bicycles) to deliver express parcels to customers. However, light vehicles are difficult to solve the problem of delivering heavy goods (usually referring to express parcels over 20 kilograms). Therefore, generally, some vehicles are organized from the urban transfer yard to directly deliver heavy goods to customers.

[0003] However, there are usually very few transfer yards in a city, with a very large coverage area. And due to management inertia, like many deliverymen configured in the blocks, each heavy goods deliveryman is specifically responsible for the heavy goods within the assigned area. However, the traditional regional delivery volume allocation scheme has too large fluctuations in delivery volume, and it is impossible to reasonably arrange delivery capacity, which brings inconvenience to delivery. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a discounted regional delivery volume optimization method, device, computer equipment, and storage medium that improve the stability of the delivery process.

[0005] An optimization method for regional delivery volume, the method includes:

[0006] Obtain a regional delivery volume optimization request, and determine the timeout area corresponding to the regional delivery volume optimization request;

[0007] Obtain the non-timeout areas adjacent to the timeout area, perform delivery optimization operations on the non-timeout areas, and obtain the time benefits corresponding to the delivery optimization operations;

[0008] According to the time benefits, construct a weighted bipartite graph maximum matching problem;

[0009] Solve the weighted bipartite graph maximum matching problem, and obtain the allocation result corresponding to the weighted bipartite graph maximum matching problem;

[0010] Optimize the regional delivery volume according to the allocation result.

[0011] In one of the embodiments, the determining the timeout area corresponding to the regional delivery volume optimization request includes:

[0012] Through a preset TSP model, determine the working time of each delivery area in the current delivery task allocation for the regional delivery volume optimization request;

[0013] Mark the delivery areas where the working hours exceed the preset working hour threshold as overtime areas.

[0014] In one embodiment, the obtaining of the non-overtime areas adjacent to the overtime areas and the performing of delivery optimization operations on the non-overtime areas to obtain the time benefits corresponding to the delivery optimization operations includes:

[0015] Obtain the non-overtime areas adjacent to the overtime areas;

[0016] Perform delivery optimization operations on the non-overtime areas through a preset VRP model, and obtain the time benefits corresponding to the delivery optimization operations.

[0017] In one embodiment, the performing of delivery optimization operations on the non-overtime areas through a preset VRP model and the obtaining of the time benefits corresponding to the delivery optimization operations includes:

[0018] Obtain the model decision variables and model constraint conditions, and construct a VRP model to minimize the total delivery distance;

[0019] Obtain the delivery optimization route by solving the VRP model;

[0020] Obtain the optimized total delivery time according to the delivery optimization route;

[0021] Obtain the time benefits corresponding to the delivery optimization operations according to the optimized total delivery time.

[0022] In one embodiment, the constructing of a weighted bipartite graph maximum matching problem according to the time benefits includes:

[0023] Construct a first point set according to the overtime areas, construct a second point set according to the non-overtime areas adjacent to the overtime areas, add a side line between the overtime areas and the non-overtime areas, and obtain the benefit weight corresponding to the side according to the time benefits;

[0024] Construct a weighted bipartite graph maximum matching problem according to the first point set, the second point set, the side line, and the benefit weight.

[0025] In one embodiment, after the regional delivery volume optimization according to the allocation result, it further includes:

[0026] Obtain the delivery route corresponding to the regional delivery volume optimization after the regional delivery volume optimization;

[0027] Feed back the delivery route.

[0028] A regional delivery volume optimization device, the device includes:

[0029] A request acquisition module, configured to acquire a regional delivery volume optimization request, and determine an overtime area corresponding to the regional delivery volume optimization request;

[0030] A delivery optimization module, configured to acquire a non-overtime area adjacent to the overtime area, perform a delivery optimization operation on the non-overtime area, and obtain a time benefit corresponding to the delivery optimization operation;

[0031] A problem construction module, configured to construct a weighted bipartite graph maximum matching problem according to the time benefit;

[0032] A delivery allocation module, configured to solve the weighted bipartite graph maximum matching problem, and obtain an allocation result corresponding to the weighted bipartite graph maximum matching problem;

[0033] A regional delivery volume optimization module, configured to optimize the regional delivery volume according to the allocation result.

[0034] In one embodiment, the determining the working time of each delivery area under the existing delivery task allocation for the regional delivery volume optimization request includes:

[0035] By constructing a TSP model, determining the working time of each delivery area under the existing delivery task allocation for the regional delivery volume optimization request;

[0036] Marking the delivery areas with working time exceeding a preset working time threshold as overtime areas.

[0037] A computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0038] Acquire a regional delivery volume optimization request, and determine an overtime area corresponding to the regional delivery volume optimization request;

[0039] Acquire a non-overtime area adjacent to the overtime area, perform a delivery optimization operation on the non-overtime area, and obtain a time benefit corresponding to the delivery optimization operation;

[0040] Construct a weighted bipartite graph maximum matching problem according to the time benefit;

[0041] Solve the weighted bipartite graph maximum matching problem, and obtain an allocation result corresponding to the weighted bipartite graph maximum matching problem;

[0042] Optimize the regional delivery volume according to the allocation result.

[0043] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0044] Obtain a request for optimizing the regional delivery volume, and determine the timeout area corresponding to the request for optimizing the regional delivery volume;

[0045] Obtain the non-timeout areas adjacent to the timeout area, perform delivery optimization operations on the non-timeout areas, and obtain the time benefits corresponding to the delivery optimization operations;

[0046] Construct a weighted bipartite graph maximum matching problem according to the time benefits;

[0047] Solve the weighted bipartite graph maximum matching problem, and obtain the allocation result corresponding to the weighted bipartite graph maximum matching problem;

[0048] Optimize the regional delivery volume according to the allocation result.

[0049] The above regional delivery volume optimization method, device, computer device and storage medium determine the timeout area corresponding to the regional delivery volume optimization request by obtaining the regional delivery volume optimization request; obtain the non-timeout areas adjacent to the timeout area, perform delivery optimization operations on the non-timeout areas, and obtain the time benefits corresponding to the delivery optimization operations; construct a weighted bipartite graph maximum matching problem according to the time benefits; solve the weighted bipartite graph maximum matching problem, and obtain the allocation result corresponding to the weighted bipartite graph maximum matching problem; optimize the regional delivery volume according to the allocation result. Through determining the timeout area and non-timeout area corresponding to the delivery, and then performing delivery optimization and delivery allocation based on the benefits, and through the dynamic programming of the delivery, the delivery transportation capacity is more reasonably allocated, and the processing efficiency of the delivery process is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is an application environment diagram of the regional delivery volume optimization method in an embodiment;

[0051] Figure 2 It is a flowchart of the regional delivery volume optimization method in an embodiment;

[0052] Figure 3 In an embodiment Figure 2 It is a sub-flowchart of step 201 in

[0053] Figure 4 In an embodiment Figure 2 It is a sub-flowchart of step 203 in

[0054] Figure 5 It is a schematic diagram of the transportation route corresponding to the regional delivery volume optimization before optimization in an embodiment;

[0055] Figure 6 It is a schematic diagram of an optimized transportation route corresponding to the optimized regional delivery volume in an embodiment;

[0056] Figure 7 In an embodiment Figure 2 It is a schematic diagram of the sub - process of step 205 in

[0057] Figure 8 It is a structural block diagram of a regional delivery volume optimization device in an embodiment;

[0058] Figure 9 It is an internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The regional delivery volume optimization method provided by the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the regional delivery volume optimization server 104 through a network. Specifically, the express delivery staff can submit a regional delivery volume optimization request from the terminal 102. The regional delivery volume optimization server 104 obtains the regional delivery volume optimization request, determines the overtime area corresponding to the regional delivery volume optimization request; obtains the non - overtime areas adjacent to the overtime area, performs a delivery optimization operation on the non - overtime areas, and obtains the time benefit corresponding to the delivery optimization operation; constructs a weighted bipartite graph maximum matching problem according to the time benefit; solves the weighted bipartite graph maximum matching problem to obtain the allocation result corresponding to the weighted bipartite graph maximum matching problem; and optimizes the regional delivery volume according to the allocation result. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, and portable wearable devices, and the regional delivery volume optimization server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0061] In an embodiment, as Figure 2 shown, a regional delivery volume optimization method is provided. Taking the method applied to the regional delivery volume optimization server 104 in Figure 1 as an example, it includes the following steps:

[0062] Step 201, obtain a regional delivery volume optimization request, and determine the overtime area corresponding to the regional delivery volume optimization request.

[0063] Among them, the regional delivery volume optimization request is a request submitted by the delivery staff to the regional delivery volume optimization server 104 through the terminal during the daily delivery process when the delivery process needs to be optimized. The regional delivery volume optimization request may include the existing delivery plan, such as the estimated residence time at each delivery point, the transportation time between all pairs of delivery points, and the maximum delivery time, etc. The overtime area refers to the area where the freight transportation time may exceed the maximum delivery time due to too many cargo delivery nodes in the existing regional delivery volume optimization.

[0064] Specifically, in the cargo delivery of heavy cargo (usually express parcels over 20 kilograms), generally, the urban transfer station organizes some vehicles to directly deliver the heavy cargo to the customers. At this time, generally one vehicle is responsible for delivering the heavy cargo in a region. However, the volume usually fluctuates continuously every day. And the volume of heavy cargo is less during the day, so the fluctuation range of the heavy cargo volume is more extreme. When there are too many goods in a region, due to the long freight transportation distance, the delivery time of the goods in this region may exceed the maximum working time. And at this time, the regional delivery volume optimization method of this application can be used to perform dynamic programming on the delivery process with fluctuating shipment volumes in multiple regions to ensure the overall processing efficiency of the delivery process. First, it is possible to determine which regions will experience overtime in the existing regional delivery volume optimization in the delivery regions that need to be processed currently, and then divide these regions into overtime areas.

[0065] Step 203: Obtain the non-overtime areas adjacent to the overtime areas, perform delivery optimization operations on the non-overtime areas, and obtain the time benefit corresponding to the delivery optimization operations.

[0066] Among them, for the non-overtime areas adjacent to the overtime areas, it can be considered that their delivery volume is relatively small. Therefore, some tasks in the overtime areas can be assigned to the freight vehicles in the non-overtime areas. Based on this as the processing basis, delivery optimization operations are carried out. That is, in the delivery problem in this step, only the driving routes of two vehicles are optimized. The delivery vehicle A in the non-overtime area needs to complete all the tasks assigned to it and at the same time complete some tasks of the delivery vehicle B in the overtime area. In addition, the routes of both vehicles need to meet the constraint of not exceeding the maximum working time. The time benefit refers to how much time can be saved in the regional delivery volume optimization after the delivery optimization compared to the original regional delivery volume optimization process.

[0067] Specifically, after determining the overtime areas and non-overtime areas, some delivery tasks in the overtime areas can be assigned to the freight vehicles in the non-overtime areas. This processing process is the delivery optimization. After implementing the plan of the delivery optimization process, the time benefit existing in the delivery optimization process can be solved. The delivery optimization operation here specifically refers to performing a delivery optimization operation on each pair of overtime areas and adjacent non-overtime areas.

[0068] Step 205: Construct a weighted bipartite graph maximum matching problem according to the time benefit.

[0069] Step 207: Solve the weighted bipartite graph maximum matching problem to obtain the allocation result corresponding to the weighted bipartite graph maximum matching problem.

[0070] Step 209: Optimize the regional delivery volume according to the allocation result.

[0071] Among them, a bipartite graph means that a set of vertex sets can be divided into two parts, and the vertices within each part are not connected to each other, and there can be edges between the vertices of the two parts. Bipartite graph matching means that given a bipartite graph G, in a subgraph M of the bipartite graph G, any two edges in the edge set {E} of M do not attach to the same vertex, then M is called a matching. The maximum bipartite graph matching is to find a subset for all the edges of the bipartite graph, and this subset can meet two major conditions. The first condition is that any two edges do not depend on the same point, and the second condition is that the edges of this subset are as many as possible under the condition of meeting the first condition. The weighted bipartite graph maximum matching problem refers to solving the maximum matching problem of the bipartite graph after adding corresponding weights to the edges in the bipartite graph. Since the benefits after reallocation are calculated for each pair of adjacent overtime regions and non-overtime regions, and there is no reallocation between overtime regions and non-overtime regions, this process can be specifically abstracted as a weighted bipartite graph maximum matching problem.

[0072] Specifically, when optimization is performed and after sufficient allocation, a weighted bipartite graph maximum matching problem can be constructed based on the time corresponding to each pair of overtime regions and non-overtime regions. Then solve it to obtain the final optimized allocation result of the regional delivery volume, and perform the final optimization of the regional delivery volume based on the delivery allocation result. In one embodiment, the KM algorithm can be specifically used to solve or an integer programming model can be established to solve the weighted bipartite graph maximum matching problem to obtain the final optimized result of the regional delivery volume.

[0073] The above regional delivery volume optimization method includes obtaining a regional delivery volume optimization request; determining the overtime regions corresponding to the regional delivery volume optimization request; obtaining the non-overtime regions adjacent to the overtime regions, performing delivery optimization operations on the non-overtime regions, and obtaining the time benefits corresponding to the delivery optimization operations; constructing a weighted bipartite graph maximum matching problem according to the time benefits, and obtaining the allocation result corresponding to the weighted bipartite graph maximum matching problem; and optimizing the regional delivery volume according to the allocation result. This application determines the overtime regions and non-overtime regions corresponding to the delivery, and then performs delivery optimization and delivery allocation based on the benefits. Through the dynamic programming of the delivery, the risk brought by the sudden increase in the delivery volume is reduced, and the stability of the delivery process is improved.

[0074] In one embodiment, such asFigure 3 As shown, step 201 includes:

[0075] Step 302, by constructing a TSP model, determine the working time of each delivery area under the existing delivery task assignment for the regional delivery volume optimization request.

[0076] Step 304, mark the delivery areas with working time exceeding the preset working time threshold as overtime areas.

[0077] Among them, TSP is the Traveling Salesman Problem. Suppose there is a traveling salesman who wants to visit n cities. He must choose the path to take. The limitation of the path is that each city can only be visited once, and finally he has to return to the original city where he started. In the actual process, the delivery vehicle needs to start from the delivery distribution transfer center, go to each delivery point that needs to be delivered, and stay for a period of time. Finally, it returns to the delivery distribution transfer center. Therefore, by constructing the corresponding TSP model and solving it, the delivery time of each area can be determined. And the preset working time threshold is the maximum working time of the freight vehicle.

[0078] Specifically, after the terminal submits the estimated stay time of each delivery point, the pairwise transportation time between all delivery points, and the maximum delivery time required for calculation through the regional delivery volume optimization request, the regional delivery volume optimization server 104 can construct a model of the TSP problem based on the existing delivery area division, solve the model, and determine the delivery time corresponding to each area. And by comparing the delivery time and the maximum working time of the freight vehicle, it can be determined which of the input working areas belong to the overtime areas, so as to carry out subsequent regional delivery volume optimization. In this embodiment, the TSP model is used to solve the delivery time of the existing plan in the regional delivery volume optimization, so as to accurately confirm the overtime areas in the delivery areas, which effectively lays the foundation for the subsequent regional delivery volume optimization process.

[0079] Such as Figure 4 As shown, in one embodiment, step 203 includes:

[0080] Step 401, obtain the non-overtime areas adjacent to the overtime areas.

[0081] Step 403, perform delivery optimization operations on the non-overtime areas through the VRP model, and obtain the time benefits corresponding to the delivery optimization operations.

[0082] Among them, VRP, i.e., Vehicle Routing Problem, refers to the problem of a certain number of customers with different quantities of goods demands. A distribution center provides goods to customers, and a fleet is responsible for delivering the goods. An appropriate driving route is organized. The goal is to meet the customers' demands and achieve objectives such as the shortest distance, the minimum cost, and the least time consumption under certain constraints. In this application, the VRP model specifically solves the route planning scheme with the goal of minimizing the overall time consumption of each pair of overtime areas and non-overtime areas under the constraint of the maximum working time.

[0083] Specifically, the optimization operation of the freight route can be implemented using the VRP model. By stipulating the maximum delivery time and the number of freight vehicles, and at the same time specifying that the original volume of parcels in adjacent areas is delivered by the same vehicle. Finally, the benefit after this optimization is calculated according to the total reduced delivery time after optimization. In this embodiment, the task allocation of the overtime area and the non-overtime area can be optimized by constructing a VRP model to achieve the effect of improving the overall efficiency.

[0084] In one of the embodiments, step 403 includes: obtaining the model decision variables and the model constraint conditions, constructing a VRP model with the goal of minimizing the total delivery distance; obtaining the optimized delivery route by solving the VRP model; obtaining the total optimized delivery time according to the optimized delivery route; obtaining the time benefit corresponding to the optimized delivery operation according to the total optimized delivery time.

[0085] Among them, the model decision variables and the model constraint conditions respectively represent the variables that can be controlled in the model and the conditions that need to be satisfied for model solving. The model decision variables specifically include: : Whether the edge from node to node is selected by the vehicle; : The sequence number of the node being visited. And the model constraint conditions specifically include three constraints: in-degree and out-degree constraint: except for the initial node, the in-degree and out-degree of each node are both 1; the in-degree and out-degree of the initial node are both the number of vehicles used (=2); access sequence constraint: for the nodes visited first, the sequence number must be less than that of the nodes visited later; except for the initial node, for the nodes visited by the vehicle with a smaller sequence number, the sequence number must be less than that of the nodes visited by the vehicle with a larger sequence number; working time constraint: the working duration of each vehicle does not exceed the maximum working duration.

[0086] Specifically, by limiting the model decision variables and the model constraint conditions, a corresponding VRP model can be constructed with the goal of minimizing the total delivery distance. By solving the VRP model, the delivery routes of the overtime area and the non-overtime area adjacent to the overtime area are optimized. So that the nodes in some overtime areas are processed by the freight vehicles in the non-overtime area. Specifically, reference can be made to Figure 5 and Figure 6Among them, Figure 5 is the optimization of the regional delivery volume before optimization, while Figure 6 is the optimization of the regional delivery volume in the overtime area and non-overtime area after VRP optimization. In this embodiment, a VRP model is constructed through model decision variables and model constraint conditions, etc., and solved to optimize the task allocation in the overtime area and non-overtime area, so as to achieve the effect of improving the overall efficiency.

[0087] For example Figure 7 As shown, in one embodiment, step 205 includes:

[0088] Step 702, construct a first point set according to the overtime area, construct a second point set according to the non-overtime area adjacent to the overtime area, add a side line between the overtime area and the non-overtime area, and obtain the revenue weight corresponding to the side according to the time revenue.

[0089] Step 704, construct a weighted bipartite graph maximum matching problem according to the first point set, the second point set, the side line and the revenue weight.

[0090] Step 706, solve the weighted bipartite graph maximum matching problem by integer programming to obtain the allocation result.

[0091] Specifically, the weighted bipartite graph maximum matching problem can specifically construct a first point set according to the overtime area, construct a second point set according to the non-overtime area adjacent to the overtime area, add a side line between the overtime area and the non-overtime area, and obtain the weight corresponding to the side according to the time revenue. Thus, on the basis of the first point set, the second point set, the side line and the revenue weight, a weighted bipartite graph maximum matching problem is constructed, so as to transform the problem of selecting the maximum optimization scheme in the regional delivery volume optimization into a weighted bipartite graph maximum matching problem. Solve this weighted bipartite graph maximum matching problem by the method of integer programming. In one embodiment, in solving this weighted bipartite graph maximum matching problem by integer programming, the important parameters in the integer programming model specifically include decision variables: : Whether the edge from node to node is selected. Constraint conditions: Degree constraint: The degree of each node is at most 1. Optimization objective: Main objective: Maximize the points in the set of overtime areas that are matched; Secondary objective: Maximize the weight of the edges selected in the matching. In another embodiment, the weighted bipartite graph maximum matching problem can be solved by the KM algorithm. In this embodiment, by constructing a weighted bipartite graph maximum matching problem and then solving it to obtain the final delivery dynamic programming scheme, it can be ensured that the final regional delivery volume optimization scheme can obtain the highest time revenue, and ensure the efficiency and timeliness of delivery.

[0092] In one embodiment, after step 209, the method further includes: obtaining a delivery route corresponding to the optimized regional delivery volume; and feeding back the delivery route.

[0093] Specifically, after the optimization of the regional delivery volume is completed, the final delivery route can be obtained based on the finally determined planning scheme. Specifically, the finally determined planning scheme is a set of point sets corresponding to the overtime area and the non-overtime area respectively. The delivery route corresponding to the optimized regional delivery volume can be determined through the determined point set. Then, by feeding back the delivery route to the terminal 102, the delivery staff on the terminal 102 side can perform corresponding actual delivery processing. In this embodiment, by obtaining the delivery route according to the optimized regional delivery volume, it can effectively ensure that the result of the optimized regional delivery volume is effectively utilized and the feasibility of the scheme is ensured.

[0094] It should be understood that although Figure 2-7 the steps in the flowchart of Figure 2-7 are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover,

[0095] In one embodiment, as Figure 8 shown, a device for optimizing regional delivery volume is provided, including:

[0096] A request acquisition module 801, configured to obtain a request for optimizing regional delivery volume and determine an overtime area corresponding to the request for optimizing regional delivery volume.

[0097] A delivery optimization module 803, configured to obtain non-overtime areas adjacent to the overtime area, perform a delivery optimization operation on the non-overtime areas, and obtain the time benefit corresponding to the delivery optimization operation.

[0098] A problem construction module 805, configured to construct a weighted bipartite graph maximum matching problem according to the time benefit.

[0099] A delivery allocation module 807, configured to solve the weighted bipartite graph maximum matching problem and obtain an allocation result corresponding to the weighted bipartite graph maximum matching problem;

[0100] A regional delivery volume optimization module 809, configured to optimize the regional delivery volume according to the allocation result.

[0101] In one embodiment, the request acquisition module 801 is specifically configured to: determine the working hours of each delivery area under the existing delivery task assignment for the regional delivery volume optimization request by constructing a TSP model;

[0102] Mark the delivery areas with working hours exceeding the preset working hour threshold as overtime areas.

[0103] In one embodiment, the delivery optimization module 803 is specifically configured to: obtain the non-overtime areas adjacent to the overtime areas; perform delivery optimization operations on the non-overtime areas through a VRP model, and obtain the time benefit corresponding to the delivery optimization operations.

[0104] In one embodiment, the delivery optimization module 803 is further configured to: obtain the model decision variables and model constraint conditions, construct a VRP model to minimize the total delivery distance; obtain the optimized delivery route by solving the VRP model; obtain the optimized total delivery time according to the optimized delivery route; obtain the time benefit corresponding to the delivery optimization operations according to the optimized total delivery time.

[0105] In one embodiment, the delivery assignment module 807 is configured to: construct a first point set according to the overtime areas, construct a second point set according to the non-overtime areas adjacent to the overtime areas, add a side line between the overtime areas and the non-overtime areas, and obtain the benefit weight corresponding to the side according to the time benefit; construct a weighted bipartite graph maximum matching problem according to the first point set, the second point set, the side line, and the benefit weight; solve the weighted bipartite graph maximum matching problem through integer programming to obtain the assignment result.

[0106] In one embodiment, it further includes a route determination module, configured to: obtain the delivery route corresponding to the regional delivery volume optimization; feedback the delivery route.

[0107] For the specific limitations on the regional delivery volume optimization device, reference can be made to the limitations on the regional delivery volume optimization method in the foregoing text, which will not be elaborated here. Each module in the above regional delivery volume optimization device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above respective modules.

[0108] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 9As shown in the figure. The computer device includes a processor, a memory, and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store regional delivery volume optimization data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a method for optimizing regional delivery volume.

[0109] Those skilled in the art can understand that Figure 9 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0110] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0111] Obtain a regional delivery volume optimization request, and determine the timeout area corresponding to the regional delivery volume optimization request;

[0112] Obtain the non-timeout areas adjacent to the timeout area, perform delivery optimization operations on the non-timeout areas, and obtain the time benefit corresponding to the delivery optimization operations;

[0113] According to the time benefit, construct a weighted bipartite graph maximum matching problem;

[0114] Solve the weighted bipartite graph maximum matching problem, and obtain the allocation result corresponding to the weighted bipartite graph maximum matching problem;

[0115] Optimize the regional delivery volume according to the allocation result.

[0116] In one embodiment, when the processor executes the computer program, the following steps are further implemented: Determine the working time of each delivery area in the current delivery task allocation for the regional delivery volume optimization request through a preset TSP model; Mark the delivery areas whose working time exceeds the preset working time threshold as timeout areas.

[0117] In one embodiment, when the processor executes the computer program, the following steps are further implemented: Obtain the non-timeout areas adjacent to the timeout area; Perform delivery optimization operations on the non-timeout areas through a preset VRP model, and obtain the time benefit corresponding to the delivery optimization operations.

[0118] In one embodiment, when the processor executes the computer program, the following steps are further implemented: obtaining model decision variables and model constraint conditions, constructing a VRP model to minimize the total delivery distance; obtaining an optimized delivery route by solving the VRP model; obtaining the optimized total delivery time according to the optimized delivery route; and obtaining the time benefit corresponding to the optimized delivery operation according to the optimized total delivery time.

[0119] In one embodiment, when the processor executes the computer program, the following steps are further implemented: constructing a first point set according to the overtime area, constructing a second point set according to the non-overtime area adjacent to the overtime area, adding a sideline between the overtime area and the non-overtime area, and obtaining the benefit weight corresponding to the edge according to the time benefit; constructing a weighted bipartite graph maximum matching problem according to the first point set, the second point set, the sideline, and the benefit weight.

[0120] In one embodiment, when the processor executes the computer program, the following steps are further implemented: obtaining the delivery route corresponding to the optimized regional delivery volume after optimizing the regional delivery volume; and feeding back the delivery route.

[0121] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0122] Obtaining a regional delivery volume optimization request and determining the overtime area corresponding to the regional delivery volume optimization request;

[0123] Obtaining the non-overtime area adjacent to the overtime area, performing an optimized delivery operation on the non-overtime area, and obtaining the time benefit corresponding to the optimized delivery operation;

[0124] Constructing a weighted bipartite graph maximum matching problem according to the time benefit;

[0125] Solving the weighted bipartite graph maximum matching problem and obtaining the allocation result corresponding to the weighted bipartite graph maximum matching problem;

[0126] Performing regional delivery volume optimization according to the allocation result.

[0127] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: determining the working time of each delivery area under the current delivery task allocation for the regional delivery volume optimization request through a preset TSP model; and marking the delivery areas with a working time exceeding the preset working time threshold as overtime areas.

[0128] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: obtaining the non-overtime area adjacent to the overtime area; performing an optimized delivery operation on the non-overtime area through a preset VRP model, and obtaining the time benefit corresponding to the optimized delivery operation.

[0129] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: obtaining model decision variables and model constraint conditions, constructing a VRP model to minimize the total delivery distance; obtaining an optimized delivery route by solving the VRP model; obtaining an optimized total delivery time according to the optimized delivery route; and obtaining a time benefit corresponding to the optimized delivery operation according to the optimized total delivery time.

[0130] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: constructing a first point set according to the overtime area, constructing a second point set according to the non-overtime area adjacent to the overtime area, adding a side line between the overtime area and the non-overtime area, and obtaining a benefit weight corresponding to the side according to the time benefit; constructing a weighted bipartite graph maximum matching problem according to the first point set, the second point set, the side line, and the benefit weight.

[0131] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: obtaining a delivery route corresponding to the optimized regional delivery volume after optimizing the regional delivery volume; and feeding back the delivery route.

[0132] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0133] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0134] The above embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for optimizing regional delivery volume, the method comprises: Obtain a regional delivery volume optimization request, and determine the timeout area corresponding to the regional delivery volume optimization request; Obtain the non-timeout areas adjacent to the timeout area; Obtain model decision variables and model constraint conditions, and construct a VRP model to minimize the total delivery distance; obtain an optimized delivery route by solving the VRP model; Obtain the optimized total delivery time according to the optimized delivery route; obtain the time benefit corresponding to the delivery optimization operation according to the optimized total delivery time; Construct a first point set according to the timeout area, construct a second point set according to the non-timeout areas adjacent to the timeout area, add a side line between the timeout area and the non-timeout areas, and obtain the benefit weight corresponding to the side according to the time benefit; Construct a weighted bipartite graph maximum matching problem according to the first point set, the second point set, the side line and the benefit weight; solve the weighted bipartite graph maximum matching problem, and obtain the allocation result corresponding to the weighted bipartite graph maximum matching problem; Optimize the regional delivery volume according to the allocation result.

2. The method according to claim 1, wherein, the determination of the timeout area corresponding to the regional delivery volume optimization request includes: Determine the working time of each delivery area in the current delivery task allocation of the regional delivery volume optimization request through a preset TSP model; Mark the delivery areas with working time exceeding the preset working time threshold as timeout areas.

3. The method according to claim 1, wherein, after optimizing the regional delivery volume according to the allocation result, it further includes: Obtain the delivery route corresponding to the optimized regional delivery volume after the regional delivery volume optimization; Feedback the delivery route.

4. The method according to claim 1, wherein, the solution of the weighted bipartite graph maximum matching problem to obtain the allocation result corresponding to the weighted bipartite graph maximum matching problem includes: Establish an integer programming model to solve the weighted bipartite graph maximum matching problem, and obtain the allocation result corresponding to the weighted bipartite graph maximum matching problem.

5. A device for optimizing regional delivery volume, wherein, the device includes: A request acquisition module, configured to acquire a regional delivery volume optimization request and determine the timeout area corresponding to the regional delivery volume optimization request; A delivery optimization module, configured to acquire the non-timeout areas adjacent to the timeout area; acquire model decision variables and model constraint conditions, construct a VRP model to minimize the total delivery distance; obtain an optimized delivery route by solving the VRP model; obtain the optimized total delivery time according to the optimized delivery route; obtain the time benefit corresponding to the delivery optimization operation according to the optimized total delivery time; A problem construction module, configured to construct a first point set according to the timeout area, construct a second point set according to the non-timeout area adjacent to the timeout area, add a sideline between the timeout area and the non-timeout area, and obtain the revenue weight corresponding to the edge according to the time revenue; construct a weighted bipartite graph maximum matching problem according to the first point set, the second point set, the sideline, and the revenue weight; A delivery assignment module, configured to solve the weighted bipartite graph maximum matching problem and obtain the assignment result corresponding to the weighted bipartite graph maximum matching problem; A regional delivery volume optimization module, configured to optimize the regional delivery volume according to the assignment result.

6. The apparatus according to claim 5, wherein, the step of determining the working time of each delivery area for the regional delivery volume optimization request under the existing delivery task assignment includes: determining the working time of each delivery area for the regional delivery volume optimization request under the existing delivery task assignment by constructing a TSP model; marking the delivery areas with working time exceeding the preset working time threshold as timeout areas.

7. The apparatus according to claim 5, wherein, it further includes a route determination module, configured to: obtain the delivery route corresponding to the optimized regional delivery volume after the regional delivery volume optimization; feedback the delivery route.

8. The apparatus according to claim 5, wherein, the delivery assignment module is specifically configured to: establish an integer programming model to solve the weighted bipartite graph maximum matching problem and obtain the assignment result corresponding to the weighted bipartite graph maximum matching problem.

9. A computer device, including a memory and a processor, where the memory stores a computer program, wherein, when the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

Images