Distribution path optimization method and device, equipment and storage medium

Abstract

The invention discloses a distribution path optimization method and device, equipment and a storage medium, and relates to the technical field of logistics scheduling, and the method comprises the steps: obtaining parameter information corresponding to each to-be-distributed order; the parameter information and a pre-constructed mixed integer programming model are combined to construct a limitation main problem, and the mixed integer programming model minimizes the total distribution cost on the basis of meeting the requirement that each client accesses a single service; and carrying out iterative solution on the limitation main problem to obtain an optimal distribution route. According to the method, on the basis of meeting the requirement that each client accesses a single service, the total distribution cost is minimized, the mixed integer programming model is constructed, linear relaxation is carried out on the mixed integer programming model, the limitation main problem is obtained, iteration solution is carried out on the limitation main problem, and a feasible scheduling scheme with the lowest cost is obtained.

Description

Technical Field

[0001] The present invention relates to the field of logistics scheduling technology, and in particular to a distribution path optimization method, device, equipment and storage medium. Background Art

[0002] With the accelerated pace of life and the advancement of technology, transportation services are becoming more and more convenient. In the distribution process, speed has become a key factor in maintaining customer satisfaction. Therefore, distribution platforms face the challenge of ensuring on-time delivery. To solve this problem, they usually strive to complete delivery before the promised delivery time of each order. In order to provide high-quality services, these platforms usually rely on a large number of transportation staff to complete orders. At present, the multi-trip container trailer problem (MT-CDP-TP) is an important research topic in the field of logistics. This problem is very complicated in practical applications because multiple factors need to be considered, including vehicle capacity, travel restrictions, cargo loading and unloading sequence, etc. Therefore, logistics companies urgently need a path planning algorithm framework that solves multiple vehicles (enterprise distribution fleets) and multiple constraints (time, cost, demand, etc.). Summary of the invention

[0003] Based on this, it is necessary to provide a distribution path optimization method, device, equipment and storage medium for the above-mentioned technical problems, so as to solve at least one of the above-mentioned technical problems.

[0004] The present invention provides a distribution path optimization method, comprising:

[0005] Get the parameter information corresponding to each order to be delivered;

[0006] Combining the parameter information with a pre-constructed mixed integer programming model, constructing a restricted master problem, wherein the mixed integer programming model minimizes the total delivery cost on the basis of satisfying each customer's single visit to the service;

[0007] The restricted main problem is solved iteratively to obtain the optimal delivery route.

[0008] According to a delivery path optimization method provided by the present invention, the iterative solution of the constraint master problem to obtain the optimal delivery route includes:

[0009] Step S131, solving the restricted main problem to obtain a current solution;

[0010] Step S132, combining the current solution, solving the dual problem corresponding to the restricted main problem, so as to determine whether there is a new delivery route according to the solution result;

[0011] Step S133, if there is a new delivery route, then add the new delivery route to the restricted main problem;

[0012] Step S134, constructing a valid inequality, and adding the valid inequality to the restricted main problem, resolving the new restricted main problem to obtain a new current solution, and for the new current solution: repeating steps S132 to S134 until no new delivery route is found, and executing step S135;

[0013] Step S135, determining whether the current solution is an integer solution;

[0014] Step S136, if it is an integer solution, taking the current solution as the optimal delivery route;

[0015] Step S137: If it is not an integer solution, based on the current solution, a preset branch and bound algorithm is used to solve and obtain the optimal delivery route.

[0016] According to a delivery path optimization method provided by the present invention, based on the current solution, a preset branch and bound algorithm is used to solve and obtain an optimal solution, and the optimal solution is used as the optimal delivery route, including:

[0017] Based on the current solution, a branch variable is selected, wherein the branch variable refers to a variable whose value of the current solution is farthest from the integer 0 and the integer 1;

[0018] Based on the branch variables, two sub-problems are constructed, wherein the variable values ​​of the two sub-problems are set to 0 and 1 respectively;

[0019] Each sub-problem is solved iteratively until all variables of the current solution are integer solutions, and the integer solution is used as the optimal delivery route.

[0020] According to a delivery route optimization method provided by the present invention, the constructing effective inequality comprises:

[0021] Define the undirected conflict graph G ′ (V ′ ,E ′ ), each vertex r∈V ′ Represents a column or route, binary constant α j , is 1 if and only if edge (i, j) is used by route r∈V′, otherwise it is 0;

[0022] The edge set is defined as follows:

[0023]

[0024] An edge e∈E′ exists if two columns p and q have at least one row with a coefficient equal to 1; this means that two routes conflict if they visit at least one of the same customers.

[0025] Define group C as Where N(r) is the set of task nodes traversed by route r; the clique inequality is:

[0026]

[0027] A cycle in an undirected conflict graph G′ is defined as a set of vertices that form a Hamiltonian circuit. A cycle that visits an odd number of vertices V leads to the odd-hole inequality:

[0028]

[0029] Where G′ is a set of cycles that visit an odd number of vertices in the graph G′;

[0030] make is the minimum covering set, G″ is the minimum covering set of all routes generated by each branch and bound node, where The minimum covering inequality is:

[0031]

[0032] According to a delivery route optimization method provided by the present invention, solving the dual problem corresponding to the restricted master problem to determine whether there is a new delivery route according to the solution result includes:

[0033] For any unvisited node:

[0034] Determine label information associated with the node, wherein the label information includes path information, cost information, check number and constraint conditions of the node:

[0035] Based on the label information of the node, checking whether the node satisfies all known constraint conditions and whether it satisfies preset boundary conditions;

[0036] If the constraint conditions and boundary conditions are met, a new path is formed based on the label information of the node;

[0037] Determine whether the new path is a complete path and whether the inspection number corresponding to the new path is less than a preset numerical threshold;

[0038] If it is a complete path and the number of checks is less than a preset numerical threshold, it is determined that the solution result contains a new delivery route.

[0039] According to a delivery route optimization method provided by the present invention, judging whether there is a new delivery route according to the solution result includes:

[0040] If there is no new delivery route, execute step S135: determine whether the current solution is an integer solution.

[0041] According to a distribution path optimization method provided by the present invention, the parameter information and the pre-constructed mixed integer programming model are combined to construct a restricted main problem, including:

[0042] In combination with the parameter information, linear relaxation is performed on the original problem of the mixed integer programming model to obtain the restricted master problem, wherein the value range of the variables in the mixed integer programming model is an integer variable between 0 and 1; the value range of the variables in the restricted master problem is a real number variable between 0 and 1.

[0043] The present invention also provides a distribution path optimization device, comprising:

[0044] The acquisition module is used to obtain the parameter information corresponding to each order to be delivered;

[0045] A construction module, used for combining the parameter information and a mixed integer programming model to construct a restricted main problem, wherein the mixed integer programming model minimizes the total delivery cost on the basis of satisfying each customer's single visit service;

[0046] The solution module is used to iteratively solve the restricted main problem to obtain the optimal delivery route.

[0047] The present invention also provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the above-mentioned distribution path optimization method when executing the computer-readable instructions.

[0048] The present invention also provides one or more readable storage media storing computer-readable instructions, and when the computer-readable instructions are executed by a processor, the above-mentioned distribution path optimization method is implemented.

[0049] The above-mentioned delivery path optimization method, device, equipment and storage medium include: obtaining parameter information corresponding to each order to be delivered; combining the parameter information and a pre-constructed mixed integer programming model to construct a restricted main problem, wherein the mixed integer programming model minimizes the total delivery cost on the basis of satisfying each customer's single service visit; iteratively solving the restricted main problem to obtain the optimal delivery route. The present invention constructs a mixed integer programming model by minimizing the total delivery cost on the basis of satisfying each customer's single service visit, and linearly relaxes the mixed integer programming model to obtain the restricted main problem. In addition, the branch pricing and cutting algorithm is used in combination with an effective cutting strategy to iteratively solve the restricted main problem to improve the calculation speed of the algorithm and obtain the lowest cost feasible scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0051] Figure 1 is a flow chart of a delivery path optimization method according to an embodiment of the present invention;

[0052] Figure 2 is a structural schematic diagram of a distribution path optimization device in one embodiment of the present invention;

[0053] Figure 3 is a schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The terms used in one or more embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of the present invention. The singular forms of "a", "said" and "the" used in one or more embodiments of the present invention are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of the present invention refers to and includes any or all possible combinations of one or more associated listed items.

[0056] In one embodiment, specifically, Figure 1 As shown, Figure 1 1 is a flow chart of a method for optimizing a delivery path in an embodiment of the present invention. The present invention provides a method for optimizing a delivery path, comprising the following steps:

[0057] Step S11, obtaining parameter information corresponding to each order to be delivered;

[0058] It should be noted that the parameter information includes information such as the delivery node location and the pickup node location, the warehouse node location, fuel cost and labor cost.

[0059] Step S12, combining the parameter information and a pre-built mixed integer programming model to construct a restricted master problem;

[0060] It should be noted that the mixed integer programming model minimizes the total delivery cost on the basis of satisfying each customer's single service visit. In this embodiment, the construction process of the mixed integer programming model (main problem (MP)) is as follows:

[0061] Symbol definition:

[0062] N d and N p : represent the collection of delivery nodes and pickup nodes respectively;

[0063] 0: indicates a warehouse node;

[0064] n: represents the number of customers, each customer corresponds to a delivery node i∈N d and a pickup node n+i∈N p ;

[0065] C=N d ∪N p : represents the collection of all task nodes;

[0066] G = (N, A) is a directed graph, N = {0} ∪ N d ∪N p , A represents the set of arcs;

[0067] Each arc (i, j)∈A has a travel time t ij and unit travel cost c ij associated;

[0068] Each driver has a maximum capacity Q, which can handle L trucks in platoon mode, and a maximum working time T;

[0069] δ: represents the fuel cost saving factor of the following trucks in the platoon;

[0070] Problem description:

[0071] Each task node is visited once, and there is a time precedence constraint between the two task nodes of each customer node;

[0072] The driver completes a dispatch involving two routes (i.e., multiple trips) in one working day;

[0073] The goal is to find the feasible dispatch path with the lowest cost;

[0074] The model is described as follows:

[0075] Define x rh ∈{0,1}: indicates that if route r is served in schedule h, it is 1, otherwise it is 0;

[0076] Parameter air Indicates whether node i is on route r;

[0077] τ r represents the time span of route r;

[0078] Objective function:

[0079] Minimize total cost: including fuel cost and labor cost, etc.

[0080] According to the problem setting, the mixed integer programming model is established as follows:

[0081]

[0082] The above formula represents minimizing the sum of the selected path costs.

[0083]

[0084] Among them, the above formulas respectively indicate that each customer point is required to be visited once; the total duration of each schedule h is less than T; and the value range of the defined variable is 0 or 1.

[0085] Further, using (That is, the number of times path r is selected is equal to the number of times path r is used in all schedules h), the above problem can be transformed into the following form:

[0086]

[0087] Among them, the above formulas respectively represent minimizing the sum of all path costs; each customer point is required to be visited once; the sum of the lengths of all paths is less than the sum of the lengths of m schedules; and the variable value range is defined as 0 or 1.

[0088] Specifically, in a mixed integer programming (MIP) model, the original problem includes integer variables, which usually represent binary choices of decisions, for example, 0 or 1 represents "no" or "yes", or "do not use" or "use". In order to construct a restricted master problem, the original problem of the mixed integer programming model is linearly relaxed in combination with the parameter information, which means that the integer variables are replaced by continuous real variables whose values ​​range from 0 to 1, and the restricted master problem is obtained, wherein the restricted master problem is a linear programming (LP) problem, which can be solved by any commercial solver.

[0089] Step S13, iteratively solving the constraint main problem to obtain the optimal delivery route.

[0090] In one embodiment, the constraint master problem is iteratively solved to obtain the optimal delivery route, including:

[0091] Step S131, solving the restricted main problem to obtain a current solution;

[0092] Step S132, combining the current solution, solving the dual problem corresponding to the restricted master problem by using a column generation method, so as to determine whether there is a new delivery route according to the solution result;

[0093] Step S133, if there is a new delivery route, then add the new delivery route to the restricted main problem;

[0094] Step S134, constructing a valid inequality, and adding the valid inequality to the restricted main problem, resolving the new restricted main problem to obtain a new current solution, repeating steps S132 to S134 until no new delivery route is found, and executing step S135;

[0095] Step S135, determining whether the current solution is an integer solution;

[0096] Step S136, if it is an integer solution, taking the current solution as the optimal delivery route;

[0097] Step S137: If it is not an integer solution, based on the current solution, a preset branch and bound algorithm is used to solve and obtain the optimal delivery route.

[0098] Specifically, the restricted main problem is solved to obtain the current solution. For example, the restricted main problem is solved by using a heuristic algorithm such as an ant colony algorithm or a greedy algorithm to obtain the current solution, thereby ensuring the quality of the initial solution and reducing the reliance on the high-quality upper bound. Further, in combination with the current solution, a preset column generation algorithm is used to solve the dual problem corresponding to the restricted main problem. For example, the current solution (LP relaxation solution) is used as the starting point of the column generation algorithm to determine whether there is a new delivery route based on the solution result. It should be noted that the column generation algorithm is a heuristic method for solving large-scale mixed integer programming problems. Optionally, this method is particularly suitable for problems where the number of variables and constraints is so large that a complete MIP model cannot be directly constructed. The column generation algorithm works by the following steps: Start with a small subproblem, usually containing only those variables that are known to be most likely to be selected. When the solution to the subproblem is infeasible or the quality of the infeasible solution does not meet the requirements, the algorithm will try to add new columns (variables) to the subproblem. Each time a new column is added, the constraints of the main problem are updated and a new subproblem is solved; the process is repeated until a satisfactory solution is found or no more columns can be generated. In linear programming (LP), for every primal problem, there is a dual problem. The dual problem consists of the constraints of the primal problem and the dual variables. New delivery routes refer to new delivery solutions generated in the subproblem by adding new columns (variables) to the main problem. These new columns represent possible delivery routes or service points that may improve the efficiency of the entire system or reduce costs while satisfying all constraints.

[0099] Furthermore, if there is a new delivery route, the new delivery route is added to the restricted main problem; in addition, a valid inequality is constructed, wherein the construction process of the valid inequality is specifically described in the following embodiment and will not be repeated here. Then, the valid inequality is added to the restricted main problem, and optionally, it is necessary to ensure that the new columns and inequalities are compatible with the existing constraints, and the new restricted main problem is solved again to obtain a new current solution, and for the new current solution: steps S132 to S134 are repeatedly executed until no new delivery routes are found, or the newly added columns and valid inequalities no longer improve the quality of the solution, thereby taking the final current solution as the optimal solution.

[0100] Furthermore, after finding no new delivery routes or the newly added columns and valid inequalities no longer improve the quality of the solution, it is determined whether the current solution is an integer solution; if it is an integer solution, the current solution is used as the optimal delivery route; if it is not an integer solution, it is proved that some variables in the delivery route (such as the delivery quantity of each delivery location) may be decimals, and it is necessary to convert the current non-integer solution into an integer solution so that the optimal delivery route can be found. Then, based on the current solution, a branch variable is selected, wherein the branch variable refers to the variable whose value of the current solution is farthest from the integer 0 and the integer 1. Further, based on the branch variable, two sub-problems are constructed, wherein the variable values ​​of the two sub-problems are set to 0 and 1 respectively; for each sub-problem, each sub-problem is iteratively solved until all variables of the current solution are integer solutions, and the integer solution is used as the optimal delivery route. The specific solution process is specifically described in the following embodiment and will not be repeated here.

[0101] The embodiment of the present invention constructs a mixed integer programming model by minimizing the total delivery cost on the basis of satisfying each customer's single service visit, and linearly relaxes the mixed integer programming model to obtain a restricted main problem. In addition, a branch pricing and cutting algorithm is used, combined with an effective column generation strategy and a method for constructing effective inequalities, to iteratively solve the restricted main problem and obtain a feasible scheduling solution with the lowest cost, so as to improve the calculation speed of the algorithm.

[0102] In one embodiment of the present invention, constructing a valid inequality includes:

[0103] Define the undirected conflict graph G ′ (V ′ ,E ′ ), each vertex r∈V ′ Represents a column or route, binary constant α j , is 1 if and only if edge (i, j) is used by route r∈V′, otherwise it is 0;

[0104] The edge set is defined as follows:

[0105]

[0106] An edge e∈E′ exists if two columns p and q have at least one row with a coefficient equal to 1; this means that two routes conflict if they visit at least one of the same customers.

[0107] Define group C as Where N(r) is the set of task nodes traversed by route r; the clique inequality is:

[0108]

[0109] A cycle in an undirected conflict graph G′ is defined as a set of vertices that form a Hamiltonian circuit. A cycle that visits an odd number of vertices V leads to the odd-hole inequality:

[0110]

[0111] Where G′ is a set of cycles that visit an odd number of vertices in the graph G′;

[0112] make is the minimum covering set, G″ is the minimum covering set of all routes generated by each branch and bound node, where The minimum covering inequality is:

[0113]

[0114] Specifically, in this embodiment, the linear relaxation is improved by the effective inequality derived from the conflict graph.

[0115] Define the undirected conflict graph G ′ (V ′ ,E ′ ): Every vertex r∈V ′ Represents a column or route, binary constant α j , which is 1 if and only if edge (i, j) is used by route r∈V′, and 0 otherwise.

[0116] The edge set is defined as:

[0117]

[0118] That is, if two columns p and q have at least one row with a coefficient equal to 1, then there is an edge e∈E′. If they visit at least one of the same customers, then there is an edge. The valid inequalities based on the conflict graph are as follows.

[0119] (I) Group inequality

[0120] Define group C as Where N(r) is the set of task nodes traversed by route r. The clique inequality is:

[0121]

[0122] (II) Strange Hole Inequality

[0123] A cycle in a conflict graph G′ is defined as a set of vertices that form a Hamiltonian circuit. A cycle that visits an odd number of vertices V leads to the odd-hole inequality:

[0124]

[0125] Where G′ is a set of cycles that visit an odd number of vertices in graph G′.

[0126] (III) Minimum Covering Inequality

[0127] make is the minimum covering set, G″ is the minimum covering set of all routes generated by each branch and bound node, where The minimum covering inequality is:

[0128]

[0129] These valid inequalities are tested and added to the model in a sequential manner, thereby transforming the integer variable ξ r After linear relaxation, the new form of the model is obtained by combining the effective inequalities as shown below:

[0130]

[0131] Among them, the above formulas respectively represent minimizing the total path cost; each customer point is visited once in total); the sum of the lengths of all paths is less than the sum of the lengths of m schedules; group inequality; odd hole inequality; minimum cover inequality; variable definition: whether the path is selected, the variable value range is a real variable from 0 to 1. In addition, the dual problem of the above problem is:

[0132]

[0133] u=(u0,u1…,u n ), is the dual variable vector. The new problem after combining the effective inequalities is used as the new restricted main problem, the dual problem is the subproblem, and the effective inequality set is empty during the initial iteration.

[0134] The embodiment of the present invention strengthens the linear relaxation of the model by introducing effective inequalities based on conflict graphs in graph theory, such as clique inequality, odd hole inequality and minimum cover inequality, thereby reducing the search space and improving the calculation speed of the algorithm.

[0135] In one embodiment of the present invention, solving the dual problem corresponding to the restricted master problem to determine whether a new delivery route exists according to the solution result includes:

[0136] For any unvisited node: determine the label information associated with the node, wherein the label information includes the path information, cost information, test number and constraints of the node: based on the label information of the node, check whether the node satisfies all known constraints and whether it satisfies preset boundary conditions; if the constraints and boundary conditions are satisfied, form a new path based on the label information of the node; determine whether the new path belongs to a complete path, and whether the test number corresponding to the new path is less than a preset numerical threshold; if it belongs to a complete path and the test number is less than the preset numerical threshold, determine that the solution result contains a new delivery route.

[0137] It should be noted that for the original linear programming problem, a corresponding dual problem is constructed. The variables of the dual problem are the shadow prices corresponding to the constraints of the original problem. In one embodiment, a linear programming solver is used to solve the dual problem to obtain a dual solution, including the shadow prices and the values ​​of the slack variables or the remaining variables. Among them, the shadow prices in the dual solution can be regarded as test numbers, which reflect the degree of relaxation of the constraints.

[0138] Specifically, all unvisited nodes are traversed, and the following steps are performed for any unvisited node: obtain the associated label information, including the node's path information, cost information, test number and constraint conditions. Further, check whether the label information of the node satisfies all known constraints, and check whether the test number of the node satisfies the preset boundary conditions. If the label information of the node satisfies the constraints and boundary conditions, a new path is formed based on the label information of the node. Further, it is determined whether the formed path belongs to a complete path, that is, whether it covers all nodes and constraints. For a complete new path, the corresponding test number is calculated. It is determined whether the test number of the path is less than a preset numerical threshold, for example, whether the test number is less than 0. If the new path is complete and the corresponding test number is less than the preset threshold, it is determined that there is a new delivery route. In other embodiments, a depth-first traversal is performed starting from a certain node: when a complete node is traversed, the node is marked as visited, and then all unvisited adjacent nodes of the node are tried to be visited as the starting point of the new path until all nodes are visited or a solution is found, effectively improving the calculation speed of the algorithm.

[0139] In one embodiment of the present invention, based on the current solution, a preset branch and bound algorithm is used to solve and obtain an optimal solution, and the optimal solution is used as the optimal delivery route, including:

[0140] Based on the current solution, a branch variable is selected, wherein the branch variable refers to the variable whose value of the current solution is farthest from the integer 0 and the integer 1; based on the branch variable, two sub-problems are constructed, wherein the variable values ​​of the two sub-problems are set to 0 and 1 respectively; each sub-problem is iteratively solved until all variables of the current solution are integer solutions, and the integer solutions are used as the optimal delivery route.

[0141] Specifically, according to the variable value of the current solution, a branch variable is selected. The branch variable is the variable farthest from the integer 0 or 1 in the current solution. According to the selected branch variable xi, two sub-problems are constructed, optionally, sub-problem 1: set the value of the branch variable xi to 0. Sub-problem 2: set the value of the branch variable xi to 1. For each sub-problem, a linear programming (LP) or integer linear programming (ILP) method is used to solve, for example, using an existing optimization library (such as CPLEX, Gurobi or open source GLPK) to solve. During the solution process, if it is found that the solution of a sub-problem does not meet the constraints, or the obtained solution is not better than the current solution, pruning can be performed, that is, the branch is no longer explored until all variables take integer values, or no more sub-problems can be generated (that is, all branches have been explored), thereby obtaining an optimal delivery route, which is the solution when all variables take integer values. During the solution process, the optimal current solution is kept and updated when a better solution is found.

[0142] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0143] In one embodiment, a distribution path optimization device is provided, and the distribution path optimization device corresponds to the distribution path optimization method in the above embodiment. Figure 2 As shown, Figure 2 : is a schematic diagram of a structure of a distribution path optimization device in one embodiment of the present invention, the distribution path optimization device comprises:

[0144] The acquisition module 21 is used to obtain parameter information corresponding to each order to be delivered;

[0145] A construction module 22 is used to construct a restricted main problem by combining the parameter information and a mixed integer programming model, wherein the mixed integer programming model minimizes the total delivery cost on the basis of satisfying each customer's single visit service;

[0146] The solution module 23 is used to iteratively solve the constraint main problem to obtain the optimal delivery route.

[0147] The specific definition of the distribution path optimization device can be found in the definition of the distribution path optimization method above, which will not be repeated here. Each module in the above distribution path optimization device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0148] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, Figure 3 : is a schematic diagram of a computer device in one embodiment of the present invention. The computer device includes a processor, a memory, a network interface and a database connected by a device 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 readable storage medium and an internal memory. The readable storage medium stores an operating device, a computer-readable instruction and a database. The internal memory provides an environment for the operation of the operating device and the computer-readable instructions in the readable storage medium. The database of the computer device is used to store data involved in the distribution path optimization method. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instruction is executed by the processor, a distribution path optimization method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0149] In one embodiment, a computer device is provided. The computer device may be a terminal device, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, and a network interface connected through a device 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 readable storage medium. The readable storage medium stores computer-readable instructions. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a distribution path optimization method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0150] In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, and when the processor executes the computer-readable instructions, the steps of the above-mentioned delivery route optimization method are implemented.

[0151] In one embodiment, a readable storage medium is provided, the readable storage medium stores computer-readable instructions, and the computer-readable instructions are executed by the processor to implement the steps of the above-mentioned distribution path optimization method. A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0152] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0153] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A distribution path optimization method, characterized in that: include: Get the parameter information corresponding to each order to be delivered; Combining the parameter information with a pre-constructed mixed integer programming model, constructing a restricted master problem, wherein the mixed integer programming model minimizes the total delivery cost on the basis of satisfying each customer's single visit to the service; The restricted main problem is solved iteratively to obtain the optimal delivery route.

2. The delivery route optimization method according to claim 1, characterized in that: The iterative solution of the restriction master problem to obtain the optimal delivery route includes: Step S131, solving the restricted main problem to obtain a current solution; Step S132, combining the current solution, solving the dual problem corresponding to the restricted main problem, so as to determine whether there is a new delivery route according to the solution result; Step S133, if there is a new delivery route, then add the new delivery route to the restricted main problem; Step S134, constructing a valid inequality, and adding the valid inequality to the restricted main problem, resolving the new restricted main problem to obtain a new current solution, and for the new current solution: repeating steps S132 to S134 until no new delivery route is found, and executing step S135; Step S135, determining whether the current solution is an integer solution; Step S136, if it is an integer solution, taking the current solution as the optimal delivery route; Step S137: If it is not an integer solution, based on the current solution, a preset branch and bound algorithm is used to solve and obtain the optimal delivery route.

3. The delivery route optimization method according to claim 2, characterized in that: The method of obtaining an optimal solution based on the current solution by using a preset branch and bound algorithm, and using the optimal solution as the optimal delivery route, includes: Based on the current solution, a branch variable is selected, wherein the branch variable refers to a variable whose value of the current solution is farthest from the integer 0 and the integer 1; Based on the branch variables, two sub-problems are constructed, wherein the variable values ​​of the two sub-problems are set to 0 and 1 respectively; Each sub-problem is solved iteratively until all variables of the current solution are integer solutions, and the integer solution is used as the optimal delivery route.

4. The delivery route optimization method according to claim 2, characterized in that: The constructing effective inequalities includes: Define the undirected conflict graph G ′ (V ′ ,E ′ ), each vertex r∈V ′ Represents a column or route, binary constant α j , is 1 if and only if edge (i, j) is used by route r∈V′, otherwise it is 0; The edge set is defined as follows: An edge e∈E′ exists if two columns p and q have at least one row with a coefficient equal to 1; this means that two routes conflict if they visit at least one of the same customers. Define group C as Where N(r) is the set of task nodes traversed by route r; the clique inequality is: A cycle in an undirected conflict graph G′ is defined as a set of vertices that form a Hamiltonian circuit. A cycle that visits an odd number of vertices V leads to the odd-hole inequality: Where G′ is a set of cycles that visit an odd number of vertices in the graph G′; make is the minimum covering set, G″ is the minimum covering set of all routes generated by each branch and bound node, where The minimum covering inequality is:

5. The delivery route optimization method according to claim 2, characterized in that: Solving the dual problem corresponding to the restricted main problem to determine whether a new delivery route exists according to the solution result includes: For any unvisited node: Determine label information associated with the node, wherein the label information includes path information, cost information, check number and constraint conditions of the node: Based on the label information of the node, checking whether the node satisfies all known constraint conditions and whether it satisfies preset boundary conditions; If the constraint conditions and boundary conditions are met, a new path is formed based on the label information of the node; Determine whether the new path is a complete path and whether the inspection number corresponding to the new path is less than a preset numerical threshold; If it is a complete path and the number of checks is less than a preset numerical threshold, it is determined that the solution result contains a new delivery route.

6. The delivery route optimization method according to claim 1, characterized in that: The determining whether there is a new delivery route according to the solution result includes: If there is no new delivery route, execute step S135: determine whether the current solution is an integer solution.

7. The delivery route optimization method according to claim 1, characterized in that: The step of combining the parameter information with a pre-built mixed integer programming model to construct a restricted master problem includes: In combination with the parameter information, linear relaxation is performed on the original problem of the mixed integer programming model to obtain the restricted master problem, wherein the value range of the variables in the mixed integer programming model is an integer variable between 0 and 1; the value range of the variables in the restricted master problem is a real number variable between 0 and 1.

8. A distribution path optimization device, characterized in that: include: The acquisition module is used to obtain the parameter information corresponding to each order to be delivered; A construction module, used for combining the parameter information and a mixed integer programming model to construct a restricted main problem, wherein the mixed integer programming model minimizes the total delivery cost on the basis of satisfying each customer's single visit service; The solution module is used to iteratively solve the restricted main problem to obtain the optimal delivery route.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executed on the processor, characterized in that: When the processor executes the computer-readable instructions, the distribution path optimization method according to any one of claims 1 to 7 is implemented.

10. A readable storage medium having computer readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, the delivery path optimization method according to any one of claims 1 to 7 is implemented.

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