Express transportation network design method, device and equipment

Abstract

The invention relates to the technical field of operational research, in particular to an express transportation network design method, device and equipment, and the method comprises the steps: determining a decision variable according to an express package transportation demand; identifying the number of vehicles on each feasible vehicle path of the decision variable, the number of packages on each feasible package path and the distribution mechanism of the vehicle capacity on each sub-path; constructing a vehicle space-time network according to the number of vehicles on each feasible vehicle path, constructing a parcel space-time network according to the number of parcels on each feasible parcel path, and establishing a double space-time network for express transportation according to the vehicle space-time network, the parcel space-time network, the distribution mechanism of the vehicle capacity on each sub-path, the transfer limitation and the transfer cost. And determining a drivable path set of the vehicle and the parcel, and generating an express transportation network design model to meet the transportation demand. Therefore, the problems of difficulty in balancing transportation cost and efficiency, complex network design, insufficient data processing capability and the like in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the technical field of operations research, and in particular to a method, device and equipment for designing an express transportation network. Background Art

[0002] With the booming development of e-commerce, the express delivery industry is booming at an unprecedented speed, which has not only profoundly changed people's shopping methods, but also greatly promoted the innovation and transformation of the logistics industry. However, behind this rapid growth, there are also the dual challenges of transportation cost control and efficiency improvement, which puts higher requirements on express delivery companies.

[0003] Faced with huge transportation volumes and complex transportation networks, transportation costs have become an important issue that express delivery companies cannot avoid. Traditionally, express delivery companies rely on freight consolidation systems to reduce costs and achieve economies of scale in bulk transportation by merging small shipments. However, the indispensable transshipment link in this process has become a major problem in cost control. Transshipment not only requires additional labor and equipment investment, but also increases time costs, making the entire transportation chain more complicated and difficult to manage.

[0004] In order to meet this challenge, the problem of transport network design has been explored, seeking to optimize the transport process, improve transport efficiency and reduce transport costs through more sophisticated models and algorithms. However, traditional models are unable to cope with the complex and changing reality of China's express delivery industry. These models are often based on simplified assumptions, such as ignoring transshipment costs or limiting vehicles to direct transport between two points, which cannot accurately reflect the complexity and dynamics of the actual transport process. Summary of the invention

[0005] The present application provides a method, device and equipment for designing an express transportation network to solve the problems in the prior art such as difficulty in balancing transportation cost and efficiency, complex network design and insufficient data processing capabilities.

[0006] The first aspect of the present application provides a method for designing an express transportation network, comprising the following steps: determining decision variables according to express package transportation demand, and obtaining transshipment restrictions and transshipment costs; identifying the number of vehicles on each feasible vehicle path, the number of packages on each feasible package path, and the allocation mechanism of vehicle capacity on each sub-path of the decision variables; constructing a vehicle space-time network according to the number of vehicles on each feasible vehicle path, constructing a package space-time network according to the number of packages on each feasible package path, and establishing a dual space-time network for express transportation according to the vehicle space-time network, the package space-time network, the allocation mechanism of vehicle capacity on each sub-path, transshipment restrictions and transshipment costs; determining a set of drivable paths for vehicles and packages according to the dual space-time networks, generating an express transportation network design model based on the set of drivable paths, and utilizing the express transportation network design model to meet the express package transportation demand.

[0007] Optionally, a vehicle space-time network is constructed according to the number of vehicles on each feasible vehicle path, including: enumerating all feasible vehicle paths, wherein the number of transfer centers passed by the vehicle is less than a preset value, and for the same transfer center sequence, different departure times correspond to different vehicle paths; enumerating all sub-paths of each vehicle path to form a sub-path set, and constructing a final sub-path set based on the sub-path sets corresponding to all vehicle paths, wherein a sub-path is an edge connecting two transfer center space-time nodes on the path, and all packages on the edge get on the vehicle from the starting point of the sub-path and get off the vehicle at the end point of the sub-path; and constructing a vehicle space-time network based on the final sub-path set.

[0008] Optionally, a package spatiotemporal network is constructed according to the number of packages on each feasible package path, including: obtaining a set of spatiotemporal nodes of the transfer center; according to the set of all subpaths and waiting edges on each feasible package path; and constructing a package spatiotemporal network according to the set of spatiotemporal nodes of the transfer center, the set of all subpaths and waiting edges.

[0009] Optionally, the transfer restriction is: the package cannot switch sub-paths in the transfer center where the package transfer operation cannot be performed, the transfer cost is calculated based on the number of sub-paths for each feasible package path, and the vehicle capacity is allocated to each sub-path as follows: there are x on the vehicle path p p vehicles, and the corresponding path contains n p edges, (i, j) represents the subpath connecting the i-th and j-th transfer centers, and the variable Represents the capacity ratio allocated to the subpath (i, j) on the mth edge (m-1, m), 1≤m≤n p , containing a subset of subpaths Subpath subset The capacity constraint is:

[0010]

[0011] The capacity of all vehicle paths assigned to the sub-path s = (i, j) is added together to obtain the capacity:

[0012]

[0013] If it is forbidden to load packages at the i′th transit center of path p, 1≤i′≤n p -1, then remove the variable and the i′th capacity limit constraint.

[0014] Optionally, the express transportation network design model is:

[0015]

[0016] in, For regular bus travel expenses, For overtime driving expenses, is the cost of a feasible package path, x p is the number of vehicles on vehicle path p, Represents a vehicle transportation network All feasible regular bus paths in represents all feasible paths for overtime vehicles, Represents a parcel transport network All possible package paths in Indicates x on the path p The capacity ratio of vehicles allocated to sub-path s, w k Indicates the weight of the package. represents the capacity ratio allocated to subpath (i, j), y kq is the weight of the kth demand on package path q, N f is the number of vehicles in the regular fleet f, is the set of subpaths derived from vehicle path p, n p is the number of edges contained in the vehicle path p.

[0017] Optionally, an express transportation network design model is used to meet the express parcel transportation demand, including: selecting an initial subset of vehicles and parcel paths based on the express transportation network design model, generating a restricted main problem based on the initial subset of vehicles and parcel paths, generating a pricing subproblem based on a linear relaxation solution to the restricted main problem, and generating a test number based on the pricing subproblem; if there is a negative column in the test number, generating a parcel path subproblem and a vehicle path subproblem based on a simultaneous row and column generation method, and expanding the path subset of the restricted main problem based on the solution results of the parcel path subproblem and the vehicle path subproblem; otherwise, establishing a cutting plane subproblem based on a strong inequality and a cut set inequality, establishing a branch and bound tree, and solving the cutting plane subproblem at the tree nodes of the branch and bound tree by linear relaxation, if there is a non-integer vehicle path variable, then branching the number of vehicles on the edge of the vehicle space-time network and the edge variable of the parcel space-time network, if there is an integer vehicle path variable, the express transportation network design is completed.

[0018] Alternatively, the strong inequality is:

[0019]

[0020] The cut set inequality is

[0021]

[0022] in, Indicates that all originating centers are The destination is The total weight of the package.

[0023] Optionally, a branch constraint is of the form:

[0024] ∑ p x p ≤z or ∑ p x p ≥z,

[0025] Wherein, z is a natural number.

[0026] The second aspect of the present application provides an express transportation network design device, including: a determination module, used to determine decision variables according to the express package transportation demand, and obtain transshipment restrictions and transshipment costs; an identification module, used to identify the decision variables, the number of vehicles on each feasible vehicle path, the number of packages on each feasible package path, and the allocation mechanism of vehicle capacity on each sub-path; a construction module, used to construct a vehicle space-time network according to the number of vehicles on each feasible vehicle path, construct a package space-time network according to the number of packages on each feasible package path, and establish a dual space-time network for express transportation according to the vehicle space-time network, the package space-time network, the allocation mechanism of vehicle capacity on each sub-path, transshipment restrictions and transshipment costs; a generation module, used to determine a set of drivable paths for vehicles and packages according to the dual space-time network, generate an express transportation network design model based on the drivable path set, and use the express transportation network design model to meet the express package transportation demand.

[0027] The third aspect of the present 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 program to implement the express delivery network design method as described in the above embodiment.

[0028] Therefore, this application includes the following beneficial effects:

[0029] The embodiment of the present application constructs a fine-grained model that is highly close to the actual transportation demand by accurately determining decision variables such as the paths of vehicles and packages, combining the number of vehicles and packages and the allocation mechanism of vehicle capacity. This model not only optimizes the scheduling of vehicles and packages, reduces waiting and idle time, and improves transportation efficiency, but also reduces transportation costs by considering transshipment restrictions and costs, thereby enhancing the profitability of the enterprise. At the same time, the dual-space-time network model shows strong flexibility and can cope with various transportation needs and changes to ensure the smooth completion of transportation tasks. In addition, it optimizes resource allocation, improves resource utilization, and directly improves customer satisfaction by improving transportation efficiency and reducing delays. As a result, it solves the technical problems of the difficulty in balancing transportation costs and efficiency, complex network design, and insufficient data processing capabilities in the prior art.

[0030] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0032] Figure 1 A flowchart of a method for designing an express transportation network according to an embodiment of the present application;

[0033] Figure 2 A flow chart of a branch pricing cutting algorithm provided according to an embodiment of the present application;

[0034] Figure 3 An example diagram of an express transportation network design device provided according to an embodiment of the present application;

[0035] Figure 4 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0037] As the cornerstone of the express delivery industry, transportation network design is about efficiently planning the flow of vehicles and packages, achieving transportation goals in the shortest time and reducing overall costs. Faced with a complex network of transshipment centers and diverse package demands, express delivery companies need to carefully calculate the vehicle operating costs and package transshipment fees for each route, striving for the optimal allocation of resources.

[0038] Given the significant proportion of transportation costs in the operation of express delivery companies, building and optimizing a freight consolidation system has become the key to reducing costs. The system achieves a dual improvement in transportation efficiency and frequency by combining multiple small shipments for centralized transportation. However, the transshipment process, which is transferring packages from one vehicle to another, promotes integration efficiency but also introduces additional costs including labor, equipment, and time. Therefore, how to accurately model and manage transshipment in transportation network design has become a hot topic of common concern in academia and industry.

[0039] In the modeling process, the spatial coupling between packages and vehicles and the temporal synchronization between vehicles constitute the core challenge, making the problem complex and difficult to solve. To simplify the process, some studies focus on specific scenarios where the transshipment cost is negligible, vehicles only perform point-to-point direct transportation, or the model scale is small. Although it is easy to analyze, it is difficult to directly apply it to the complex actual environment of the express delivery industry.

[0040] In view of the uniqueness of express delivery - significant transshipment costs, flexible vehicles passing through multiple centers, and a large number of vehicles and packages - traditional simplified models are unable to cope with it. To this end, scholars have explored more advanced modeling methods, such as sub-path-based models. Although such methods have achieved results in specific areas such as liner shipping, their characteristics of few predefined paths and no need to consider the time dimension are not suitable for the complexity and dynamics of express delivery networks.

[0041] The following describes the express transportation network design method, device and equipment of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the transportation network cannot be calculated in actual use mentioned in the above background technology, the present application provides an express transportation network design method, in which a fine-grained model that is highly close to the actual transportation demand is constructed by accurately determining the decision variables such as the path of the vehicle and the package, combining the number of vehicles and packages and the allocation mechanism of vehicle capacity. This model not only optimizes the scheduling of vehicles and packages, reduces waiting and idle time, and improves transportation efficiency, but also reduces transportation costs by considering transshipment restrictions and costs, and enhances the profitability of enterprises. At the same time, the dual-space-time network model shows strong flexibility, can cope with various transportation needs and changes, and ensure the smooth completion of transportation tasks. In addition, resource allocation is optimized, resource utilization is improved, and customer satisfaction is directly improved by improving transportation efficiency and reducing delays. As a result, the problems of difficulty in balancing transportation costs and efficiency, complex network design, and insufficient data processing capabilities in the prior art are solved.

[0042] Specifically, Figure 1 A flow chart of a method for designing an express transportation network provided in an embodiment of the present application.

[0043] like Figure 1 As shown, the express transportation network design method includes the following steps:

[0044] In step S101, decision variables are determined according to the express package transportation demand, and transshipment restrictions and transshipment costs are obtained.

[0045] Among them, the express parcel transportation demand can be the parcel transportation task that the express company needs to complete within a specific time period, including specific information such as the starting point, destination, estimated delivery time, parcel weight or volume of the parcel. Decision variables may include vehicle routes, parcel distribution plans, transshipment point selection, etc. Transshipment restrictions may be the conditions or restrictions set for the transfer of parcels between different vehicles or transshipment centers during transportation. Transshipment costs may be the additional costs incurred due to transshipment operations during the transportation of the parcel.

[0046] It can be understood that the embodiments of the present application help to select lower-cost and more efficient routes and transshipment methods when planning transportation plans by accurately determining decision variables, obtaining transshipment restrictions and transshipment costs, and other measures.

[0047] In step S102, the decision variables of the number of vehicles on each feasible vehicle path, the number of packages on each feasible package path, and the allocation mechanism of vehicle capacity on each sub-path are identified.

[0048] It can be understood that the embodiments of the present application can ensure that the number of required vehicles is minimized while meeting the transportation demand by identifying the number of vehicles on each feasible vehicle path; determining the number of packages on each feasible package path helps to achieve a balanced distribution of packages and reduce the transportation pressure caused by package concentration; and reasonably allocating vehicle capacity on each sub-path can ensure that the vehicle can load as many packages as possible without overloading, thereby reducing the number of transportation times and transshipment costs.

[0049] Specifically, the decision variables include the number of vehicles on each feasible vehicle path, the number of packages on each feasible package path, and the allocation of vehicle capacity on each sub-path. Assume that there are L transfer centers in the express delivery network and the decision cycle is a discrete T unit time cycle. There are K package transportation demands, each of which consists of a four-tuple <o k , d k , σ k , w k > indicates that o k , d k Respectively represent the origin and destination of the package, σ k It indicates the time when the package can be shipped, and wk indicates the weight of the package.

[0050] Using directed graphs represents the vehicle spatiotemporal network, where is the set of space-time nodes of the transfer center, is the set of edges between transit centers, is the set of waiting edges in adjacent time periods of the same transfer center. A feasible vehicle path p is a set of some end-to-end connected edges in the network. The decision variable x p Represents the number of vehicles on path p. Similarly, based on the edge set composed of subpaths of all feasible vehicle paths and the spatiotemporal node set of the transfer center, a package spatiotemporal network is constructed. Similarly, the feasible path q for the kth demand can be defined as k , the decision variable y kq ∈[0,1] represents the proportion of packages transported via this path. The subpath set of vehicle path p is defined as Decision variables Indicates x on the path p The capacity ratio of vehicles allocated to sub-path s.

[0051] In step S103, a vehicle space-time network is constructed according to the number of vehicles on each feasible vehicle path, a package space-time network is constructed according to the number of packages on each feasible package path, and a dual space-time network for express delivery is established based on the vehicle space-time network, the package space-time network, the allocation mechanism of vehicle capacity on each sub-path, transshipment restrictions and transshipment costs.

[0052] Among them, the vehicle space-time network can display information such as the routes that a vehicle may pass through in different time periods, the transfer centers where it stops, etc. The package space-time network can be the entire transportation process of the package from the starting point to the end point, including the stops and transfers at each transfer center. The dual space-time network refers to a comprehensive model that considers both the vehicle space-time network and the package space-time network.

[0053] It can be understood that the embodiments of the present application can not only significantly improve transportation efficiency by constructing a space-time network of vehicles and packages, reduce empty driving and waiting time by optimizing path selection and achieving precise matching of supply and demand, but also effectively reduce transportation costs, and reduce transportation costs and vehicle empty driving rates by reducing unnecessary transfer links and optimizing vehicle loading rates.

[0054] In an embodiment of the present application, a vehicle spatiotemporal network is constructed according to the number of vehicles on each feasible vehicle path, including: enumerating all feasible vehicle paths, wherein the number of transfer centers passed by the vehicle is less than a preset value, and for the same transfer center sequence, different departure times correspond to different vehicle paths; enumerating all sub-paths of each vehicle path to form a sub-path set, and forming a final sub-path set based on the sub-path sets corresponding to all vehicle paths, wherein a sub-path is an edge connecting two transfer center spatiotemporal nodes on the path, and all packages on the edge get on the vehicle from the starting point of the sub-path and get off the vehicle at the end point of the sub-path; and constructing a vehicle spatiotemporal network based on the final sub-path set.

[0055] Among them, the transfer center can be a transit station for packages to be transported from one area to another.

[0056] It is understandable that the embodiments of the present application ensure the comprehensiveness and cost control of transportation planning by comprehensively enumerating feasible vehicle paths and considering the restrictions of the transfer center. Through refined path division and unified processing, a solid foundation is provided for path optimization and selection. The construction of the vehicle space-time network not only significantly improves transportation efficiency, but also enhances the flexibility and decision-making efficiency of transportation planning by reducing waiting and empty driving and realizing accurate package transportation.

[0057] Specifically, in express delivery, parcels can be transported by multiple vehicles in succession. The process of changing the transport vehicle at the intermediate transfer center is called transshipment. Directly modeling transshipment means describing the correspondence between the parcel and the vehicle on each edge, which will introduce great complexity. Subpaths are introduced, and the parcel is transported by the same vehicle on each subpath, so switching subpaths means that a transshipment has occurred. Using subpaths to construct a spatiotemporal network of parcels, parcels and vehicles are decoupled in the model, reducing the complexity of the model.

[0058] Given a vehicle path p, a subpath is an edge connecting two transfer center spatiotemporal nodes on the path, and all packages on it board the vehicle at the start of the subpath and do not get off until the end of the subpath. Therefore, a package switching subpath indicates a transfer. p A total of n vehicle routes can be established for each transfer center. p (n p +1) / 2 subpaths. The process of enumerating all subpaths can be divided into the following three steps:

[0059] a. In the vehicle space-time network, enumerate all feasible vehicle routes, where the number of transfer centers that the vehicle passes through does not exceed 4. For the same sequence of transfer centers, different departure times correspond to different vehicle routes.

[0060] b. For each vehicle path p, enumerate all its subpaths to form a set

[0061] c. Collect the sub-path sets corresponding to all vehicle paths to form the final sub-path set

[0062] In an embodiment of the present application, a package spatiotemporal network is constructed according to the number of packages on each feasible package path, including: obtaining a set of spatiotemporal nodes of the transfer center; according to the set of all subpaths and waiting edges on each feasible package path; and constructing a package spatiotemporal network according to the set of spatiotemporal nodes of the transfer center, the set of all subpaths and waiting edges.

[0063] Among them, the transit center space-time node refers to the status of the transit center at a specific time point.

[0064] It can be understood that the embodiments of the present application, by constructing a parcel space-time network, can comprehensively and real-time grasp the location, time and flow status of express parcels in the road network, thereby ensuring the efficiency and accuracy of the transportation process. It not only provides a solid foundation for route optimization, but also uses advanced algorithms to reduce transportation time and costs, and greatly improves management efficiency.

[0065] Specifically, using a directed graph represents a parcel space-time network, where is the set of space-time nodes of the transfer center, is a set containing all subpaths and waiting edges, the set and The flow of packages and the corresponding package paths are represented in the package space-time network, which is decoupled from the vehicle paths in terms of network representation.

[0066] In the embodiment of the present application, the transfer restriction is that the parcel cannot switch sub-paths in the transfer center where the parcel transfer operation cannot be performed. The transfer cost is calculated based on the number of sub-paths of each feasible parcel path. The allocation mechanism of vehicle capacity on each sub-path is: there are x on the vehicle path p. p vehicles, and the corresponding path contains n p edges, with (i,j) representing the subpath connecting the i-th and j-th transit centers, and the variable Represents the capacity ratio allocated to the subpath (i, j) on the mth edge (m-1, m), 1≤m≤n p , containing a subset of subpaths Subpath subset The capacity constraint is:

[0067]

[0068] The capacity is obtained by adding the capacities of all vehicle paths assigned to the sub-path s = (i, j):

[0069]

[0070] If it is forbidden to load packages at the i′th transfer center of path p, 1≤i′≤n p-1 , then remove the variable and the i′th capacity limit constraint.

[0071] Specifically, because the package is transported by only one vehicle on a sub-path, there is no transshipment in this process. Each time a sub-path is switched in the package path, it means switching to another vehicle for transportation, so a package transshipment occurs. In the express transportation network, some transshipment centers cannot perform package transshipment operations. Packages cannot switch sub-paths in such transshipment centers. Such constraints are called transshipment restrictions. In the parcel space-time network, as long as the parcel paths that do not meet the above constraints are set as infeasible paths, the modeling of transshipment restrictions is completed. The number of transshipments during the package transportation process is equal to the number of sub-paths contained in the package path minus one. As long as the number of sub-paths of each feasible parcel path is counted, the required transshipment cost can be calculated.

[0072] The dual space-time network representation decouples the movement of vehicles and packages. However, since the capacity of a sub-path depends on the vehicle path, a capacity allocation mechanism is needed to determine the capacity ratio of each vehicle path to its sub-path. Suppose there are x on vehicle path p p vehicles, and the path contains n p edges. (i,j) represents the subpath connecting the i-th and j-th transfer centers. Indicates the capacity ratio allocated to the subpath (i, j). On the mth edge (m-1, m), 1≤m≤n p, contains a subset of subpaths The total capacity of these subpaths must be equal to x p The capacity of the car, so there are the following capacity constraints:

[0073]

[0074] The capacity is obtained by adding the capacities of all vehicle paths assigned to the sub-path s = (i, j):

[0075]

[0076] If it is forbidden to load packages at the i′th transfer center of path p, 1≤i′≤n p-1 , then remove the variable and the i′th capacity limit constraint.

[0077] In step S104, a set of drivable paths for vehicles and packages is determined according to the dual space-time network, an express transportation network design model is generated based on the drivable path set, and the express transportation network design model is used to meet the express package transportation needs.

[0078] The set of drivable paths refers to the set of all possible paths that vehicles can travel to complete package transportation tasks under the dual space-time network framework, based on traffic rules, vehicle performance, package demand and other factors.

[0079] It is understandable that the embodiment of the present application realizes a comprehensive consideration of time and space factors in the transportation environment by constructing a dual space-time network, so that the set of drivable paths for vehicles and packages can be accurately determined, which not only improves transportation efficiency, but also ensures the smooth progress of transportation tasks by reducing detours and waiting time; it also enhances real-time scheduling capabilities, enabling the system to flexibly respond to various emergencies. At the same time, the express transportation network design model generated based on the dual space-time network effectively reduces transportation costs, energy consumption and emissions by optimizing resource allocation and path planning, and promotes the development of green logistics.

[0080] Specifically, express delivery companies usually manage a group of regular buses, each of which has a specific starting point and end point and needs to travel along a path from the starting point to the end point. Represents the set of regular bus fleets. In order to meet the uncertain package transportation needs, express delivery companies can also rent overtime buses from third-party providers, and overtime buses can travel along any route.

[0081] For each regular fleet Use collection Represents a vehicle transportation network All feasible regular bus paths in . Each path All from the starting transfer center of the regular bus f Departure and finally reach the destination f Similarly, using the collection represents all feasible paths for overtime buses, which can start from any transfer center and arrive at any transfer center. represents all feasible vehicle paths.

[0082] For every package shipping need Use collection Represents a parcel transport network All possible package paths in . Each path All start from the starting transfer center o of demand k k Departure and finally reach the destination k .

[0083] In the embodiment of the present application, the express delivery network design model is:

[0084]

[0085] in, For regular bus travel expenses, For overtime driving expenses, is the cost of a feasible package path, x p is the number of vehicles on vehicle path p, Represents a vehicle transportation network All feasible regular bus paths in represents all feasible paths for overtime vehicles, Represents a parcel transport network All possible package paths in Indicates x on the path p The capacity ratio of vehicles allocated to sub-path s, w k Indicates the weight of the package. Indicates the capacity ratio allocated to subpath (i, j), y kq is the weight of the kth demand on package path q, N f is the number of vehicles in the regular fleet f, is the set of subpaths derived from vehicle path p, n p is the number of edges contained in the vehicle path p.

[0086] Specifically, in the vehicle spatiotemporal network, D a represents the path length of edge a, τ a Indicates the travel time. In the package space-time network, τ s represents the travel time of subpath s, that is, the sum of the durations of all the edges contained in it. Assume that the unit mileage costs of regular buses and overtime buses are CR and C E , the unit weight package forwarding fee is C O The transportation time penalty for a unit weight package per unit time is C T .

[0087] The cost of a feasible vehicle path is the sum of the vehicle travel costs on each edge. For a regular bus, the cost is For overtime, the fee is Similarly, the cost of a feasible package path is the sum of the package flow costs on each sub-path, including package transfer costs and time penalties, and the cost is

[0088] Mathematical expression of the express transportation network design model with transshipment restrictions and transshipment costs.

[0089]

[0090] in, For regular bus travel expenses, For overtime driving expenses, is the cost of a feasible package path, x p is the number of vehicles on vehicle path p, Represents a vehicle transportation network All feasible regular bus paths in represents all feasible paths for overtime vehicles, Represents a parcel transport network All possible package paths in Indicates x on the path p The capacity ratio of vehicles allocated to sub-path s, w k Indicates the weight of the package. Indicates the capacity ratio allocated to subpath (i, j), y kq is the weight of the kth demand on package path q, N f is the number of vehicles in the regular fleet f, is the set of subpaths derived from vehicle path p, n p is the number of edges contained in the vehicle path p;

[0091] The optimization goal is to minimize the total cost, including vehicle travel costs, package transfer costs, and package delivery time penalties. The first constraint ensures that the number of regular buses used meets the fleet requirements; the second constraint ensures that the results meet all package delivery needs; the third constraint ensures that the total weight of packages on each sub-path does not exceed the capacity of the sub-path; the fourth constraint is the capacity allocation constraint from vehicle paths to sub-paths; other constraints specify variable types.

[0092] In an embodiment of the present application, an express transportation network design model is used to meet the express parcel transportation demand, including: selecting an initial subset of vehicle and parcel paths based on the express transportation network design model, generating a restricted main problem based on the initial subset of vehicle and parcel paths, generating a pricing subproblem based on a linear relaxation solution to the restricted main problem, and generating a test number based on the pricing subproblem; if there is a negative column in the test number, then generating a parcel path subproblem and a vehicle path subproblem based on a simultaneous row and column generation method, and expanding the path subset of the restricted main problem based on the solution results of the parcel path subproblem and the vehicle path subproblem; otherwise, establishing a cutting plane subproblem based on a strong inequality and a cut set inequality, establishing a branch and bound tree, and solving the cutting plane subproblem at the tree nodes of the branch and bound tree. If there is a non-integer vehicle path variable, the number of vehicles on the edge of the vehicle space-time network and the edge variable of the parcel space-time network are branched. If there is an integer vehicle path variable, the express transportation network design is completed.

[0093] It is understandable that the embodiments of the present application achieve precise optimization of express delivery network design by constructing a complex optimization framework, combining linear relaxation, pricing subproblems, cutting plane technology and branch and bound algorithm. This framework not only ensures that vehicles and packages can be efficiently transported along the optimal or suboptimal path, effectively reducing transportation costs, but also significantly improves service quality and customer satisfaction.

[0094] In the embodiment of the present application, the strong inequality is:

[0095]

[0096] The cut set inequality is

[0097]

[0098] in, Indicates that all originating centers are The destination is The total weight of the package.

[0099] In the embodiment of the present application, the branch constraint is in the form of:

[0100] ∑ p x p ≤z or Σ p x p ≥ z,

[0101] Wherein, z is a natural number.

[0102] Specifically, a branch-pricing-cutting algorithm is designed to solve the express transportation network design problem with transshipment restrictions and transshipment costs. First, an initial subset of vehicle and package paths is selected to form a restricted main problem, its linear relaxation is solved, and its dual value is used to construct a pricing subproblem. Subsequently, the pricing subproblems of the vehicle and package paths are solved separately to generate new paths with negative test numbers and expand the path subset. For the package pricing subproblem, classical column generation is adopted; for the vehicle pricing subproblem, a simultaneous row and column generation method is developed. In addition, a dynamic cutting plane generation module is designed to improve the lower bound of the linear relaxation, a strong branching module is developed for selecting branching variables to accelerate the search for feasible solutions, and a heuristic module is developed to obtain the upper bound of the problem to assist in pruning, such as Figure 2 As shown, including the following:

[0103] S1. Pricing subproblem: Generate vehicle and package paths with negative test numbers; use γ f ,π k ,α s ≤0 and β pm denote the dual variables of the first four constraints in the express delivery network design model. In the linear relaxation problem, each column represents a vehicle path or a package path. Therefore, the pricing subproblem involves finding a feasible column with a negative test number and can be split into two subproblems: the package path subproblem and the vehicle path subproblem.

[0104] S2, package path subproblem: column generation; for a transport demand k and its corresponding package path q, variable y kq The number of tests is:

[0105]

[0106] in, is a positive value, and π k The sign is arbitrary. Therefore, only when the dual variable π k A path with a negative check number exists only when it is strictly positive. A feasible package path starts from the starting time-space node [o(k),σ(k)] of demand k and ends at a time-space node of the destination transfer center d(k). The path with the minimum check number can be obtained by directly using the Dijkstra shortest path algorithm. At any time when the Dijkstra algorithm is running, if the current cost exceeds π k , it can be terminated early. The branch-price-cut algorithm solves the corresponding package path subproblem for each demand separately, and adds the package path with the minimum negative test number to the path subset.

[0107] S3, vehicle routing sub-problem: row and column generation; for vehicle routing There are two types of variables: p and (or ). The test numbers for these two variables are:

[0108]

[0109] 0≤i<j≤n p

[0110] Among them, when p is a regular bus path, When p is an overtime route, The two sets of test numbers can be used to define the test number of vehicle path p:

[0111]

[0112] Among them, the mth and m+1th constraints are subtracted, and we have

[0113]

[0114] This means that nodes m = 1, 2, ..., n p -1, the inflow is equal to the outflow, so the minimization problem can be transformed into the flow from node 0 to node n p The shortest path problem with weight α ij ≤0.

[0115] For any subpath Use collection represents all paths with the same starting and ending points as s, that is, subpath s can be represented by Any path in is derived. The distance of subpath s is defined as:

[0116]

[0117] in, Represents the optimal path, using Indicates the path The number of edges to include.

[0118] The shortest path problem corresponding to the vehicle routing subproblem is defined in the auxiliary network The network includes the package network The node set and edge set of . Each subpath s in contains a dual value α s , distance l s , optimal path and the number of transshipment centers n s For each regular fleet The cost of subpath s is equal to C O ·l s +U·α s ; For overtime bus, the cost is C R ·ls +U·α s The shortest path problem contains resource constraints on the number of transfer centers, that is, the number of subpaths n s The sum of values ​​cannot exceed 4.

[0119] Since the weight of the edge in the shortest path problem may be negative, a labeling algorithm is designed to determine the shortest path for the overtime vehicle subproblem. For the regular vehicle path subproblem, an enumeration method is designed to calculate and compare the test number of each regular vehicle path. Assume that the final result path consists of m subpaths {s1, s2, …, s m}, the vehicle path can be expressed as The branch-price-cut algorithm solves the corresponding vehicle routing subproblem for each regular bus fleet and adds the path with the minimum negative test number to the path subset; for overtime buses, a vehicle path with the minimum negative test number is added to the path subset.

[0120] S4, cutting plane: improve the optimal value of the linear relaxation problem, where the branch-price cutting algorithm implements two cutting planes: strong inequality and cut set inequality.

[0121] Among them, the strong inequality: For any demand k, the strong inequality on the edge This imposes a lower bound on the number of vehicles:

[0122]

[0123] When the weight of the package transportation demand is much smaller than the capacity of the vehicle, the strong inequality ensures a larger number of vehicles, thus reducing the fractional solutions.

[0124] The strong inequality introduces an additional dual variable that affects the calculation of the test number. ks Denote the dual variables associated with the strong inequality, and use the set [SI] = {(k, s)} to denote the index of the current strong inequality. For each element (k, s) ∈ [SI], in the kth parcel path subproblem, -δ ks Adding to the cost of edge s maintains the structure of the parcel routing subproblem without modifying the algorithm. For the vehicle routing subproblem, for each edge ∑ k:(k,s)∈[SI] w k ·δ ks Adding it to its cost maintains the structure of the vehicle routing subproblem and does not require any modification to the algorithm.

[0125] Cut set inequality: Set the transfer center Partition into non-empty subsets and its complement Constructing cut sets In the vehicle space-time network, all the origin transfer centers are The terminal transfer center is The edges belong to the cut set use Indicates that all originating centers are The destination is The cut set inequality ensures that All edges of Flow direction Total package weight required:

[0126]

[0127] For parcel origin transfer center collection Enumerate all its subsets to form a set For each transshipment center set Cut set The relevant cutset inequalities are added to the RMP linear relaxation. In order to maintain the structure of the vehicle routing subproblem after adding the cutset inequalities, the optimal path is directly corrected and the cost of the subpath s. Represents the index of all current cut set inequalities, using represents the dual variable associated with the cut set inequality. The optimal path and subpath cost are calculated by modifying the distance of edge a in the vehicle space-time network:

[0128]

[0129] The constant C for the regular bus problem is C R , for the overtime car problem, it is C E . No modifications to the algorithm are required thereafter.

[0130] Cutting plane generation process: The generation of strong inequalities is embedded in the column generation module. During the generation process, the branch-price-cut algorithm checks the strong inequality corresponding to each binary (a, k), where and And add all the violated strong inequalities to the relaxation. If there are violated strong inequalities, re-execute the column generation module. Only when there are no negative test number paths and no violated strong inequalities, stop the pricing subproblem module and the cutting plane generation module.

[0131] S5, strong branching module: accelerate the search for integer solutions; in order to obtain feasible solutions, a branch-and-bound tree is designed, and the pricing and cutting modules are embedded in the branch-and-bound framework. After finding the optimal solution of the linear relaxation at the tree node, if there is a non-integer vehicle path variable, branching is performed and two child nodes are created.

[0132] First, the number of vehicles on the spatial edge is branched, where the spatial edge refers to all edges with the same starting and ending transfer centers in the vehicle spatiotemporal network. Once all spatial edge variables are integers, the spatiotemporal edge variables are branched. The form of the branching constraint is:

[0133] ∑ p x p ≤z or ∑ p x p ≥z, This constraint is similar to the cut set inequality, so the structure of the pricing subproblem is maintained in the same way.

[0134] In order to further improve the lower bound, a strong branching module is designed. The strong branching module tests a set of non-integer variables and selects the variables with the best branching results for branching. In the present invention, the 10 variables that are farthest from the integer are selected as candidate variables. For each candidate variable, only the RMP relaxation containing the generated columns is solved to evaluate the lower bound of each child node. Taking the weaker lower bound of the child node as the score, the strong branching module selects the variable with the largest score for branching. For tree search, the present invention uses the best lower bound first rule to select nodes in the first 20 nodes of the branch and bound tree, and uses the depth first rule for the rest of the cases.

[0135] S6. Heuristic search for feasible solutions; design a heuristic module to obtain an ideal integer solution as early as possible in the traversal process. Based on this integer solution, the size of the branch and bound tree can be reduced and the search can be accelerated. The heuristic module temporarily removes all branch constraints and directly solves the RMP containing the currently generated path. For express delivery network design problems of different sizes, different time limits can be set for the heuristic module to achieve optimal performance. The heuristic module is executed once after searching every 5 branch and bound tree nodes.

[0136] According to the express transportation network design method proposed in the embodiment of the present application, by accurately determining the decision variables such as the paths of vehicles and packages, combined with the number of vehicles and packages and the allocation mechanism of vehicle capacity, a fine-grained model that is highly close to the actual transportation demand is constructed. This model not only optimizes the scheduling of vehicles and packages, reduces waiting and idle time, and improves transportation efficiency, but also reduces transportation costs by considering transshipment restrictions and costs, thereby enhancing the profitability of the enterprise. At the same time, the dual-time and space network model shows strong flexibility and can cope with various transportation needs and changes to ensure the smooth completion of transportation tasks. In addition, resource allocation is optimized, resource utilization is improved, and customer satisfaction is directly improved by improving transportation efficiency and reducing delays. As a result, the problems of difficulty in balancing transportation costs and efficiency, complex network design, and insufficient data processing capabilities in the prior art are solved.

[0137] The following is a specific example of an express transportation network design method, the content is as follows:

[0138] 1) The problem is modeled using subpaths and dual space-time networks, and a path-based mathematical model is constructed; for the express transportation network design problem with transshipment restrictions and transshipment costs, an express transportation network design model with transshipment restrictions and transshipment costs is established for this problem. The purpose of the transportation network design problem is to determine the driving paths of vehicles and packages given the express package transportation demand, so that the total transportation cost, including vehicle driving costs, package transshipment costs, and package transportation time penalty, is minimized.

[0139] 2) Design of branch-price-cut algorithm; The problem of express transportation network design with transshipment restrictions and transshipment costs is solved by designing a branch-price-cut algorithm. The algorithm first selects an initial subset of vehicle and package paths to form a restricted main problem, solves the linear relaxation, and uses its dual value to construct a pricing subproblem. Thereafter, the pricing subproblems of the vehicle and package paths are solved separately to generate new paths with negative test numbers and expand the path subset. For the package pricing subproblem, the present invention adopts classical column generation; for the vehicle pricing subproblem, a method for simultaneous generation of rows and columns is developed. In addition, a dynamic cutting plane generation module is designed to improve the lower bound of linear relaxation, a strong branching module is developed for selecting branching variables to accelerate the search for feasible solutions, and a heuristic module is developed to obtain the upper bound of the problem to assist in pruning.

[0140] 3) Implementation of the branch-price-cut algorithm; written in JAVA programming language. During the algorithm execution, the RMP linear relaxation problem belongs to the linear programming problem, and the RMP problem solved in the heuristic module belongs to the mixed integer linear programming problem.

[0141] Specifically, the commercial solver Gurobi is used to solve the linear programming model and integer programming model in the branch-price-cut algorithm, and the proposed express transportation network design model with transshipment restrictions and transshipment costs is tested on the real data of a certain express company. The total transportation costs of vehicle and package scheduling decisions obtained by the new model and the traditional service network design model are compared. The transshipment costs of the traditional service network design model are calculated based on the obtained decisions, as shown in Table 1 below.

[0142] Table 1 Sample size of companies in multiple regions

[0143]

[0144] Table 1 shows the sample size used in the experiment, and each sample corresponds to a regional company. The regional company is responsible for sending express parcels originating from the transfer center under the jurisdiction of the region to various transfer centers across the country. For each regional company, Table 1 lists its region, the number of regional transfer centers, the number of regional transfer centers without transfer restrictions, the number of express parcel destination transfer centers, the number of parcels in demand, and the number of regular bus fleets.

[0145] The loading capacity of regular buses and overtime buses is 11 tons; the cost of running one kilometer for regular buses and overtime buses is 6 yuan and 7 yuan respectively; the package transfer fee is 0.2 yuan per kilogram; the package transportation time penalty is 0.2 yuan per kilogram per day, as shown in Table 2 below.

[0146] Table 2 Model parameters

[0147]

[0148] Table 3 is the simulation results on a 7-day sample in Jilin area, as shown in Table 3 below.

[0149] Table 3 Simulation results on a 7-day sample in Jilin region

[0150]

[0151] Table 3 lists the total transportation cost, package forwarding cost, final error bound of the branch pricing cutting algorithm, and the total transportation cost reduction ratio of this model compared to the traditional model for the traditional model and the present model. On average, this model can achieve a cost reduction performance of 1.56% compared to the traditional model.

[0152] The simulation results on regional samples of different sizes are shown in Table 4 below.

[0153] Table 4 Cost reduction ratio in different regions

[0154]

[0155] The results are the average of the sample results for 7 days. Table 4 lists the total transportation cost corresponding to the traditional model and this model, the final error bound of the branch pricing cutting algorithm, and the total transportation cost reduction ratio of this model compared to the traditional model. On average, this model achieves a cost reduction performance of more than 1% compared to the traditional model.

[0156] In summary, the subpath concept and dual space-time network framework are used to deeply model the express delivery network design problem. The remarkable feature of this model is that it directly and explicitly simulates the transit link of the package during transportation, thereby achieving a more refined characterization of the transportation process. In order to efficiently solve this complex model, an advanced algorithm based on branch pricing cutting was developed, which shows excellent performance in both optimization efficiency and solution accuracy.

[0157] Compared with the traditional service network design model, this model is not only more rigorous in theory, but also shows significant advantages in practice. It can estimate the total transportation cost more accurately, closely fit the complex and changeable situations in actual operations, and effectively avoid the deviation of cost estimation. More importantly, by optimizing the transfer strategy and route planning, it can significantly reduce the overall transportation cost and bring economic benefits to express delivery companies.

[0158] In the specific application of express delivery in various regions, the express delivery network design model that includes transshipment restrictions and transshipment costs has successfully achieved the goal of minimizing the total transportation cost. At the same time, the vehicle path planning scheme output by the model is highly consistent with the actual operation requirements, verifying the effectiveness and practicality of the model in guiding actual transportation decisions. This series of results not only highlights the advanced nature of the express delivery network design model with transshipment restrictions and transshipment costs, but also fully demonstrates the powerful ability of the branch-price-cut algorithm in solving such complex optimization problems.

[0159] Next, the express transportation network design device proposed according to the embodiment of the present application is described with reference to the accompanying drawings.

[0160] Figure 3 It is a block diagram of the express transportation network design device according to an embodiment of the present application.

[0161] like Figure 3 As shown, the express transportation network design device 10 includes: a determination module, an identification module 200, a construction module 300 and a generation module 400.

[0162] Among them, the determination module 100 is used to determine the decision variables according to the express parcel transportation demand, and obtain the transshipment restrictions and transshipment costs; the identification module 200 is used to identify the decision variables, the number of vehicles on each feasible vehicle path, the number of packages on each feasible package path, and the allocation mechanism of vehicle capacity on each sub-path; the construction module 300 is used to construct a vehicle space-time network according to the number of vehicles on each feasible vehicle path, and construct a package space-time network according to the number of packages on each feasible package path, and establish a dual space-time network for express transportation according to the vehicle space-time network, the package space-time network, the allocation mechanism of vehicle capacity on each sub-path, transshipment restrictions and transshipment costs; the generation module 400 is used to determine the set of drivable paths for vehicles and packages according to the dual space-time network, generate an express transportation network design model based on the drivable path set, and use the express transportation network design model to meet the express parcel transportation demand.

[0163] It should be noted that the aforementioned explanation of the embodiment of the express transportation network design method is also applicable to the express transportation network design device of this embodiment, and will not be repeated here.

[0164] According to the express transportation network design device proposed in the embodiment of the present application, by accurately determining the decision variables such as the paths of vehicles and packages, combined with the number of vehicles and packages and the allocation mechanism of vehicle capacity, a fine-grained model that is highly close to the actual transportation demand is constructed. This model not only optimizes the scheduling of vehicles and packages, reduces waiting and idle time, and improves transportation efficiency, but also reduces transportation costs by considering transshipment restrictions and costs, thereby enhancing the profitability of the enterprise. At the same time, the dual-space-time network model shows strong flexibility and can cope with various transportation needs and changes to ensure the smooth completion of transportation tasks. In addition, resource allocation is optimized, resource utilization is improved, and customer satisfaction is directly improved by improving transportation efficiency and reducing delays. As a result, the problems of difficulty in balancing transportation costs and efficiency, complex network design, and insufficient data processing capabilities in the prior art are solved.

[0165] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0166] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0167] When the processor 402 executes the program, the express transportation network design method provided in the above embodiment is implemented.

[0168] Furthermore, the electronic device further comprises:

[0169] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0170] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0171] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0172] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0173] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0174] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0175] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0176] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0177] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0178] It should be understood that the various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, the steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0179] A person of ordinary skill in the art may understand that all or part of the steps carried by the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the above-mentioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0180] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for designing an express transportation network, characterized in that: The following steps are involved: Determine decision variables based on express parcel transportation demand and obtain transshipment restrictions and transshipment costs; Identifying the decision variables: the number of vehicles on each feasible vehicle path, the number of packages on each feasible package path, and a mechanism for allocating vehicle capacity on each sub-path; A vehicle spatiotemporal network is constructed according to the number of vehicles on each feasible vehicle path, a parcel spatiotemporal network is constructed according to the number of parcels on each feasible parcel path, and a dual spatiotemporal network of express delivery is established according to the vehicle spatiotemporal network, the parcel spatiotemporal network, the allocation mechanism of the vehicle capacity on each sub-path, the transshipment restriction and the transshipment cost; A set of drivable paths for vehicles and packages is determined according to the dual space-time network, an express transportation network design model is generated based on the set of drivable paths, and the express transportation network design model is used to meet the express package transportation demand.

2. The express transportation network design method according to claim 1, characterized in that: The step of constructing a vehicle spatiotemporal network according to the number of vehicles on each feasible vehicle path comprises: Enumerate all feasible vehicle routes, where the number of transit centers that the vehicle passes through is less than a preset value. For the same transit center sequence, different departure times correspond to different vehicle routes. Enumerate all subpaths of each vehicle path to form a subpath set, and form a final subpath set based on the subpath sets corresponding to all vehicle paths, where the subpath is an edge connecting two transit center spatiotemporal nodes on the path, and all packages on the edge are picked up from the starting point of the subpath until they are dropped off at the end of the subpath; The vehicle spatiotemporal network is constructed according to the final sub-path set.

3. The express transportation network design method according to claim 1, characterized in that: The step of constructing a package spatiotemporal network according to the number of packages on each feasible package path includes: Get the collection of transit center space-time nodes; According to the set of all sub-paths and waiting edges on each feasible package path; The package spatiotemporal network is constructed according to the set of spatiotemporal nodes of the transfer center, the set of all subpaths and waiting edges.

4. The express transportation network design method according to claim 1, characterized in that: The transfer restriction is that the parcel cannot switch sub-paths in a transfer center where the parcel transfer operation cannot be performed. The transfer cost is calculated based on the number of sub-paths of each feasible parcel path. The allocation mechanism of the vehicle capacity on each sub-path is: There are x on the vehicle path p p vehicles, and the corresponding path contains n p edges, (i, j) represents the subpath connecting the i-th and j-th transfer centers, and the variable Represents the capacity ratio allocated to the subpath (i, j) on the mth edge (m-1, m), 1≤m≤n p , containing a subset of subpaths Subpath subset The capacity constraint is: The capacity of all vehicle paths assigned to the sub-path s = (i, j) is added together to obtain the capacity: If it is forbidden to load packages at the i′th transit center of path p, 1≤i′≤n p-1 , then remove the variable and the i′th capacity limit constraint.

5. The express transportation network design method according to claim 1, characterized in that: The express transportation network design model is: in, For regular bus travel expenses, For overtime driving expenses, is the cost of a feasible package path, x p is the number of vehicles on vehicle path p, Represents a vehicle transportation network All feasible regular bus paths in represents all feasible paths for overtime vehicles, Represents a parcel transport network All possible package paths in Indicates x on the path p The capacity ratio of vehicles allocated to sub-path s, w k Indicates the weight of the package. represents the capacity ratio allocated to subpath (i, j), y kq is the weight of the kth demand on package path q, N f is the number of vehicles in the regular fleet f, is the set of subpaths derived from vehicle path p, n p is the number of edges contained in the vehicle path p.

6. The express transportation network design method according to claim 1, characterized in that: The utilizing the express transportation network design model to meet the express parcel transportation demand includes: Based on the express transportation network design model, an initial subset of vehicles and package routes needs to be selected. Generate a restricted main problem according to the initial subset of vehicle and package paths, generate a pricing subproblem based on a linear relaxation solution of the restricted main problem, and generate a test number based on the pricing subproblem; If there is a negative column in the test number, a package path subproblem and a vehicle path subproblem are generated based on a simultaneous generation of rows and columns, and a path subset of the restricted main problem is expanded based on the solution results of the package path subproblem and the vehicle path subproblem; Otherwise, a cutting plane subproblem is established based on strong inequalities and cut set inequalities, and a branch and bound tree is established. The cutting plane subproblem is solved linearly at the tree nodes of the branch and bound tree. If there are non-integer vehicle path variables, the number of vehicles on the edges of the vehicle space-time network and the edge variables of the package space-time network are branched. If there are integer vehicle path variables, the express transportation network design is completed.

7. The express transportation network design method according to claim 6, characterized in that: The strong inequality is: The cut set inequality is in, Indicates that all originating centers are The destination is The total weight of the package.

8. The express transportation network design method according to claim 6, characterized in that: The branch constraints are of the form: ∑ p x p ≤z or Wherein, z is a natural number.

9. A device for designing an express delivery network, characterized in that: include: A determination module is used to determine decision variables based on express parcel transportation demand and obtain transshipment restrictions and transshipment costs; an identification module for identifying the decision variables, namely, the number of vehicles on each feasible vehicle path, the number of packages on each feasible package path, and a vehicle capacity allocation mechanism on each sub-path; A construction module, for constructing a vehicle spatiotemporal network according to the number of vehicles on each feasible vehicle path, constructing a parcel spatiotemporal network according to the number of parcels on each feasible parcel path, and establishing a dual spatiotemporal network of express transportation according to the vehicle spatiotemporal network, the parcel spatiotemporal network, the allocation mechanism of the vehicle capacity on each sub-path, the transshipment restriction and the transshipment cost; A generation module is used to determine a set of drivable paths for vehicles and packages based on the dual space-time network, generate an express transportation network design model based on the drivable path set, and use the express transportation network design model to meet the express package transportation needs.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the express transportation network design method according to any one of claims 1 to 8.

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