Warehousing and distribution network optimization method, equipment, storage medium and program product

Abstract

The embodiment of the present invention provides a method, device, storage medium and program product for optimizing a warehousing and distribution network, which obtains candidate sorting warehouses and relevant basic parameters in the warehousing and distribution network in the target area; constructs the objective function and constraints of the warehousing and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, and the objective function is the total cost function of the warehousing and distribution network; calls a preset solver interface to solve the objective function, determines the target candidate sorting warehouse selection scheme that makes the total cost of the warehousing and distribution network the lowest, the target warehousing construction scheme of the selected sorting warehouse, and the target distribution scheme, and optimizes the warehousing and distribution network. By constructing the objective function and constraints of the total cost of the warehousing and distribution network, calling the preset solver interface for solving, quickly and accurately determine the target candidate sorting warehouse selection scheme that makes the total cost of the warehousing and distribution network the lowest, the target warehousing construction scheme of the selected sorting warehouse, and the target distribution scheme, and more scientifically guide the optimization of the warehousing and distribution network.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of warehousing and logistics, and in particular to a warehousing and distribution network optimization method, device, storage medium and program product. Background Art

[0002] With the rapid development of e-commerce, the demand for warehousing capacity and logistics response continues to increase, and the warehousing and distribution network needs to be continuously optimized to adapt to the needs of business expansion.

[0003] Among them, the existing facility location problem model is usually used for the site selection problem of the sorting warehouse. Currently, the common facility location problem models include the P-median problem model, the P-center problem model, the covering problem model, etc.

[0004] Existing facility location problem models are usually used for site selection facility design and planning in the initial construction stage of logistics systems. With the rapid development of logistics systems, these models are difficult to meet the needs of business expansion and cannot obtain the optimal sorting warehouse location solution, thus unable to obtain the optimal warehousing and distribution network. Summary of the invention

[0005] The embodiment of the present invention provides a storage and distribution network optimization method, device, storage medium and program product for obtaining an optimal storage and distribution network.

[0006] In a first aspect, an embodiment of the present invention provides a method for optimizing a warehousing and distribution network, comprising:

[0007] Obtain candidate sorting bins within the target area;

[0008] Obtain relevant basic parameters in the warehousing and distribution network;

[0009] According to the candidate sorting warehouses and the basic parameters, construct the objective function and constraint conditions of the warehouse distribution network optimization model, wherein the objective function is the total cost function of the warehouse distribution network;

[0010] Calling a preset solver interface, solving the objective function according to the constraint conditions, determining a target candidate sorting warehouse selection scheme, a target warehousing construction scheme for the selected sorting warehouse, and a target distribution scheme that minimize the total cost of the warehousing and distribution network, so as to optimize the warehousing and distribution network according to the target candidate sorting warehouse selection scheme, the target warehousing construction scheme for the selected sorting warehouse, and the target distribution scheme.

[0011] In a second aspect, an embodiment of the present invention provides a storage and distribution network optimization device, including:

[0012] A candidate sorting bin acquisition module is used to acquire candidate sorting bins in a target area;

[0013] Basic parameter acquisition module, used to obtain relevant basic parameters in the warehousing and distribution network;

[0014] A construction module, used to construct an objective function and constraint conditions of a warehousing and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, wherein the objective function is a total cost function of the warehousing and distribution network;

[0015] A solution module is used to call a preset solution program interface, solve the objective function according to the constraint conditions, determine the target candidate sorting warehouse selection plan, the target warehousing construction plan of the selected sorting warehouse, and the target distribution plan that minimize the total cost of the warehousing and distribution network, so as to optimize the warehousing and distribution network according to the target candidate sorting warehouse selection plan, the target warehousing construction plan of the selected sorting warehouse, and the target distribution plan.

[0016] In a third aspect, an embodiment of the present invention provides an electronic device, including: at least one processor; and a memory;

[0017] The memory stores computer-executable instructions;

[0018] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method according to the first aspect.

[0019] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the method described in the first aspect is implemented.

[0020] In a fifth aspect, an embodiment of the present invention provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the method described in the first aspect.

[0021] The storage and distribution network optimization method, device, storage medium and program product provided by the embodiment of the present invention obtain the candidate sorting warehouses in the target area; obtain the relevant basic parameters in the storage and distribution network; construct the objective function and constraint conditions of the storage and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, wherein the objective function is the total cost function of the storage and distribution network; call the preset solver interface, solve the objective function according to the constraint conditions, and determine the target candidate sorting warehouse selection scheme that makes the total cost of the storage and distribution network the lowest, the target warehousing scheme of the selected sorting warehouse, and the target distribution scheme, so as to optimize the storage and distribution network according to the target candidate sorting warehouse selection scheme, the target warehousing scheme of the selected sorting warehouse, and the target distribution scheme. The embodiment of the present invention can quickly and accurately determine the target candidate sorting warehouse selection scheme that makes the total cost of the storage and distribution network the lowest, the target warehousing scheme of the selected sorting warehouse, and the target distribution scheme by constructing the objective function and constraint conditions of the total cost of the storage and distribution network, and call the preset solver interface for solution, so as to more scientifically guide the optimization of the storage and distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0023] Figure 1 A schematic diagram of an application scenario of a warehousing and distribution network optimization method provided by an embodiment of the present invention;

[0024] Figure 2 A flowchart of a storage and distribution network optimization method provided by an embodiment of the present invention;

[0025] Figure 3 A flowchart of a storage and distribution network optimization method provided by another embodiment of the present invention;

[0026] Figure 4 A schematic diagram of obtaining candidate sorting bins provided by an embodiment of the present invention;

[0027] Figure 5a , Figure 5b , Figure 5c They are respectively the optimal sorting warehouse coverage relationship diagrams of different numbers in the warehousing and distribution network provided by an embodiment of the present invention;

[0028] Figure 6 A structural diagram of a storage and distribution network optimization device provided by an embodiment of the present invention;

[0029] Figure 7 A structural diagram of an electronic device provided by an embodiment of the present invention.

[0030] The above drawings show clear embodiments of the present disclosure, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present disclosure in any way, but to illustrate the concepts of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0031] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0032] As the demand for warehousing capacity and logistics response continues to increase, the warehousing and distribution network needs to be continuously optimized to meet the needs of business expansion. Among them, the existing facility location problem model is usually used for the location selection problem of sorting warehouses. The common facility location problem models currently include the P-median problem model, the P-center problem model, the covering problem model, etc.

[0033] The P-median problem model is to select k facilities from the set of candidate facilities so that all demand nodes are served and the sum of the distances from the demand points to their nearest facilities is minimized; its mathematical model is as follows:

[0034] Objective function: min∑ i∈I ∑ j∈J c ij x ij (1.1)

[0035] Constraints: (1)∑ j∈J x ij =1;

[0036] (2)∑ j∈J y j =k (1.3)

[0037] (3)x ij ≤y j ;

[0038] (4)∑ i∈I x ij ≤y j ;

[0039] (5)x ij ∈{0,1};

[0040] (6)y j ∈{0,1};

[0041] Where I is the user node set; J is the warehouse facility node set; c ij represents the distance from user i to facility j; x ij Indicates whether facility j supplies goods to user i; y j Indicates whether facility j is put into use; k indicates the number of selected facilities.

[0042] The P-center problem model selects k facilities from the set of alternative facilities so that all demand nodes can be served and the maximum distance from each demand point to its nearest facility is minimized. The P-center problem differs from the P-median problem only in the decision-making objectives. A decision variable Z needs to be introduced to represent the maximum distance from each demand point to its nearest facility. In this way, the objective function of the P-center problem mathematical model can be expressed as minimizing the distance Z. In addition, the P-center problem model has one more constraint condition than the P-median problem model, as shown in formula (1.8).

[0043]

[0044] The coverage problem model is to select facilities so that all user nodes can be served and the number of facilities used is minimized when the set of user node numbers that can be covered by each warehouse facility is known. The mathematical model is as follows:

[0045] Objective function: min∑ j∈J y j (1.9)

[0046] Constraints: (1)

[0047] (2)y j ∈{0,1};

[0048] Among them, S j is the set of user node numbers that facility j can cover; y j Indicates whether facility j is put into use.

[0049] Existing facility location problem models are usually used for site selection facility design and planning in the initial construction stage of logistics systems. With the rapid development of logistics systems, these models are difficult to meet the needs of business expansion and cannot obtain the optimal sorting warehouse location solution, thus unable to obtain the optimal warehousing and distribution network.

[0050] Therefore, it is necessary to establish a more practical mathematical optimization model for existing business needs in order to continuously optimize the layout of the warehousing and distribution network of the supply chain system. For example, the objective function of the above model is the shortest distance or the least number of warehouses, while the cost under the existing business needs to include both the warehouse rental cost and the transportation cost; the objective function in the above model is usually a function of the distance between the facility and the user, while the transportation cost under the existing business should be a function of the number of various vehicle models used and the transportation cost of various vehicle models, where the transportation cost of various vehicle models is a piecewise function; in addition, the existing business must also meet the timeliness requirements, the number of sources of goods for users, the existing warehouse area requirements, the minimum warehouse area requirements, and the minimum warehouse expansion area requirements.

[0051] In view of the above technical problems, the present invention provides a method for optimizing a warehousing and distribution network, which comprises obtaining candidate sorting warehouses in a target area; obtaining relevant basic parameters in the warehousing and distribution network; constructing an objective function and constraint conditions of a warehousing and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, wherein the objective function is a total cost function of the warehousing and distribution network; using a preset solver to solve the objective function according to the constraint conditions, determining a target candidate sorting warehouse selection scheme, a target warehousing construction scheme of the selected sorting warehouse, and a target distribution scheme that minimize the total cost of the warehousing and distribution network, and optimizing the warehousing and distribution network according to the target candidate sorting warehouse selection scheme, the target warehousing construction scheme of the sorting warehouse, and the target distribution scheme.

[0052] The specific application scenarios of the embodiments of the present invention are as follows Figure 1 As shown, it includes an input device 101 and an electronic device 102 (such as a server). The input device 101 can obtain candidate sorting warehouses in the target area and related basic parameters in the warehousing and distribution network, and input them into the electronic device 102. The electronic device 102 constructs the objective function and constraint conditions of the warehousing and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, wherein the objective function is the total cost function of the warehousing and distribution network; a preset solver is used to solve the objective function according to the constraint conditions, and a target candidate sorting warehouse selection scheme, a target warehousing construction scheme of the selected sorting warehouse, and a target distribution scheme that minimize the total cost of the warehousing and distribution network are determined, so as to optimize the warehousing and distribution network according to the target candidate sorting warehouse selection scheme, the target warehousing construction scheme of the sorting warehouse, and the target distribution scheme.

[0053] The following specific embodiments are used to describe in detail the technical solutions of the present invention and how the technical solutions of the present application solve the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0054] Figure 2This is a flow chart of a warehouse distribution network optimization method provided by an embodiment of the present invention. This embodiment provides a warehouse distribution network optimization method, the execution subject of which is any electronic device, and the specific steps of the warehouse distribution network optimization method are as follows:

[0055] S201. Obtain candidate sorting bins in the target area.

[0056] In this embodiment, in order to optimize the storage and distribution network for a certain target area, it is necessary to obtain candidate sorting warehouses in the target area, select suitable candidate sorting warehouses, determine the warehouse construction area of ​​the selected sorting warehouse, and the distribution plan to the lower-level warehouse and distribution nodes. The candidate sorting warehouses may include existing sorting warehouses and sorting warehouses to be built.

[0057] Optional, such as Figure 3 When obtaining candidate sorting bins in the target area, the following steps may be specifically included:

[0058] S2011. Determine the target area according to the position of the lower-level warehouse distribution node of the sorting warehouse;

[0059] S2012: Divide the target area into multiple grids, and determine the center point of each grid as the location of the candidate sorting bin.

[0060] In this embodiment, it is first necessary to determine the target area. The target area can be determined based on the location of the lower-level warehouse distribution node of the sorting warehouse, for example, by obtaining the convex hull area of ​​the location of the lower-level warehouse distribution node and using the convex hull area as the target area; or the target area can be pre-set, or the user can circle the target area by himself. The lower-level warehouse distribution node of the sorting warehouse may include but is not limited to grid warehouse nodes and group leader distribution nodes. The grid warehouse is a transit station that connects the platform warehouse and offline service stores, and there can be one or more grid warehouses. The group leader distribution node is the person or distribution station responsible for distribution when groups such as communities conduct group purchases. There can be one or more group leader distribution nodes, such as Figure 4 Of course, the lower-level warehouse distribution nodes of the sorting warehouse are not limited to the two categories listed above, and may also include lower-level warehouse distribution nodes of other categories. The following embodiments only take the above two categories as examples. If other categories of lower-level warehouse distribution nodes are also included, the lower-level warehouse distribution node categories can be added for similar processing.

[0061] S202: Obtain relevant basic parameters in the warehousing and distribution network.

[0062] In this embodiment, the relevant basic parameters in the warehousing and distribution network include but are not limited to: the distance from each candidate sorting warehouse to the lower-level warehouse and distribution node, the order demand of each lower-level warehouse and distribution node, the calculation method of the transportation cost of various vehicle models, the warehouse rental cost per unit area, and the labor cost of a single order. Of course, it can also include the warehouse area of ​​each existing sorting warehouse in each candidate sorting warehouse, the warehouse efficiency, and the number of proposed partitions, etc. Some of the basic parameters are used to construct the objective function of the warehousing and distribution network optimization model, while some of the basic parameters are used to construct the constraints of the objective function. This will be described in detail in the following embodiments.

[0063] S203. Constructing the objective function and constraint conditions of the warehousing and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, wherein the objective function is the total cost function of the warehousing and distribution network.

[0064] In this embodiment, in order to obtain an optimization solution for the storage and distribution network with the lowest total cost, the total cost function of the storage and distribution network can be constructed based on the candidate sorting warehouses and basic parameters as the objective function, and constraints can be constructed to obtain the optimal solution of the objective function, that is, the optimal solution with the lowest total cost of the storage and distribution network. In this embodiment, based on a preset storage and distribution network optimization model, the basic parameters can be input into the preset storage and distribution network optimization model to automatically generate the objective function and constraints.

[0065] Among them, optionally, the total cost of the warehousing and distribution network includes warehousing cost and transportation cost, so the objective function may include a warehousing cost function and a transportation cost function, that is, the objective function may be the sum of the warehousing cost function and the transportation cost function.

[0066] The storage cost usually includes labor cost, warehouse rental cost, material cost, etc. The storage cost function can be determined based on the order demand of each subordinate warehouse distribution node, the labor cost of a single order, and the warehouse rental cost per unit area.

[0067] The transportation cost is related to the distance, vehicle type, and the transportation costs of various vehicle types. The transportation cost function can be determined based on the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle types.

[0068] The variables to be solved in the objective function are the sorting warehouse selection plan, the target warehousing plan for the sorting warehouse, and some necessary parameters in the target distribution plan, that is, decision variables, such as which sorting warehouses to select, which lower-level warehouse distribution nodes the selected sorting warehouse delivers to, the type of vehicle and quantity used for delivery, freight, the required area of ​​the selected sorting warehouse, warehouse expansion area, warehouse rental cost, material cost, etc., which can be determined according to actual needs.

[0069] S204, calling a preset solver interface, solving the objective function according to the constraint conditions, and determining a target candidate sorting warehouse selection scheme, a target warehousing construction scheme for the selected sorting warehouse, and a target distribution scheme that minimize the total cost of the warehousing and distribution network, so as to optimize the warehousing and distribution network according to the target candidate sorting warehouse selection scheme, the target warehousing construction scheme for the selected sorting warehouse, and the target distribution scheme.

[0070] In this embodiment, after the objective function and constraints are constructed, a solver can be used to solve the optimal solution of the objective function according to the constraints, that is, to obtain the optimal decision variables with the lowest total cost of the warehousing and distribution network, thereby determining the target candidate sorting warehouse selection plan, the target warehousing construction plan of the selected sorting warehouse, and the target distribution plan that minimize the total cost of the warehousing and distribution network, and then the warehousing and distribution network can be optimized based on the target candidate sorting warehouse selection plan, the target warehousing construction plan of the selected sorting warehouse, and the target distribution plan.

[0071] Optionally, in this embodiment, a mixed integer linear programming solver such as CPLEX, Gurobi, SCIP, etc. can be used to solve the objective function according to the constraints by calling the program interface of the solver. The decision variables, constraints and objective function are written into the solver, and after running, the optimal solution of the decision variables in the objective function can be obtained, which has high calculation speed and accuracy.

[0072] The storage and distribution network optimization method provided in this embodiment obtains candidate sorting warehouses in the target area; obtains relevant basic parameters in the storage and distribution network; constructs the objective function and constraint conditions of the storage and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, wherein the objective function is the total cost function of the storage and distribution network; calls the preset solver interface, solves the objective function according to the constraint conditions, and determines the target candidate sorting warehouse selection scheme that makes the total cost of the storage and distribution network the lowest, the target warehousing construction scheme of the selected sorting warehouse, and the target distribution scheme, so as to optimize the storage and distribution network according to the target candidate sorting warehouse selection scheme, the target warehousing construction scheme of the selected sorting warehouse, and the target distribution scheme. This embodiment constructs the objective function and constraint conditions of the total cost of the storage and distribution network, and calls the preset solver interface for solution, so as to quickly and accurately determine the target candidate sorting warehouse selection scheme that makes the total cost of the storage and distribution network the lowest, the target warehousing construction scheme of the selected sorting warehouse, and the target distribution scheme, thereby more scientifically guiding the optimization of the storage and distribution network.

[0073] On the basis of any of the above embodiments, taking the lower-level warehouse distribution nodes of the sorting warehouse including the grid warehouse node and the group leader distribution node as an example, the process of constructing the objective function and constraint conditions of the warehousing and distribution network optimization model, as well as the specific expressions of the objective function and constraint conditions are introduced in detail.

[0074] In this embodiment, the relevant basic parameters in the warehousing and distribution network may include: the distance from each candidate sorting warehouse to the lower-level warehousing and distribution node, the order demand of each lower-level warehousing and distribution node, the calculation method of the transportation cost of various types of vehicles, the warehouse rental cost per unit area, the labor cost of a single order, the warehouse area of ​​each existing sorting warehouse in each candidate sorting warehouse, the warehouse efficiency, and the number of planned partitions, etc.

[0075] In this embodiment, when constructing the objective function, the decision variables of the objective function, that is, the variables to be solved, may be determined first. As an example, the decision variables of the objective function may be specifically shown in Table 1:

[0076] Table 1 Decision variables of the objective function and their interpretation

[0077]

[0078] Among them, there are M candidate sorting bins. There are N grid warehouses in total. There are L group leaders in total. There are pn kinds of goods, product types There are 1 types of models from candidate sorting warehouse to grid warehouse. There are 2 types of models from candidate sorting warehouse to group leader.

[0079] Furthermore, the warehousing cost function can be determined based on the order demand of each lower-level warehousing and distribution node, the labor cost of a single order, and the warehouse rental cost per unit area; the transportation cost function can be determined based on the distance from each candidate sorting warehouse to the lower-level warehousing and distribution node and the calculation method of the transportation cost of various vehicle models; the sum of the warehousing cost function and the transportation cost function is determined as the objective function of the warehousing and distribution network optimization model.

[0080] The warehousing cost function includes the labor cost sub-function W1, the warehouse rental cost sub-function W2 and the material cost sub-function W3, and the transportation cost may include the transportation cost W4 of the selected sorting warehouse to each grid warehouse and the transportation cost W5 of the selected sorting warehouse to the group leader's distribution node.

[0081] Therefore, the expression of the objective function can be shown as follows:

[0082] Min(W1+W2+W3+W4+W5) (3.1)

[0083] Among them, for the labor cost sub-function W1, the labor cost sub-function W1 can be determined according to the order requirements of each lower-level warehouse distribution node and the labor cost of a single order, that is, the labor cost is equal to the total order amount multiplied by the labor cost of each order. The expression of the labor cost sub-function W1 is as follows:

[0084]

[0085] Among them, demand jp represents the order quantity of the product type p in the grid warehouse j, demand jp The number of orders required by the representative k for the product type p; c1 represents the labor cost of each order;

[0086] For the warehouse rental cost sub-function W2, the warehouse rental cost sub-function W2 is determined according to the warehouse rental cost per unit area and the basic warehouse building area and the warehouse expansion area. Its expression is as follows:

[0087]

[0088] Among them, f represents the basic warehouse construction area, g represents the unit warehouse expansion area. For example, f can be 5,000 square meters, and g can be 2,500 square meters. If a candidate sorting warehouse needs to be built, a minimum of 5,000 square meters is required. If 5,000 square meters is not enough, it can be expanded on the basis of 5,000 square meters in multiples of 2,500 square meters; c2 is the warehouse rental cost per unit area.

[0089] For the material cost sub-function W3, since the material cost can usually be considered as a multiple of the labor cost and the warehouse rental cost, the material cost sub-function W3 is determined according to the preset multiple of the sum of the labor cost sub-function and the warehouse rental cost sub-function. Its expression is as follows:

[0090] W3=h*(W1+W2) (3.13)

[0091] Among them, h is a preset multiple, which can be 1.11.

[0092] For the transportation cost W4 of the selected sorting warehouse to each grid warehouse, and the transportation cost W5 of the selected sorting warehouse to the group leader's distribution node, the transportation cost sub-function of a single vehicle of any vehicle model from any candidate sorting warehouse to any lower-level warehouse distribution node can be determined according to the distance from each candidate sorting warehouse to the lower-level warehouse distribution node, and the calculation method of the transportation cost of various vehicle models; according to the transportation cost sub-function, the total transportation cost functions W4 and W5 for the same type of lower-level warehouse distribution nodes are determined respectively, and the specific expressions are as follows:

[0093]

[0094]

[0095] Among them, PtG ijq is the transportation cost sub-function corresponding to the vehicle type q required when the candidate sorting warehouse i delivers goods to the grid warehouse j, PtC ikqIt is the transportation cost sub-function corresponding to the vehicle type q required when the candidate sorting warehouse i delivers goods to the group leader k. It is related to the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle types. For details, see Formulas 3.33 and 3.34.

[0096] In addition, the constraints of the warehouse distribution network optimization model in this embodiment include one or more of the following:

[0097] The number of selected sorting bins is equal to the number of proposed partitions;

[0098] The selected sorting warehouse supplies at least one lower-level warehouse distribution node, and the unselected sorting warehouse cannot supply any lower-level warehouse distribution node;

[0099] Any lower-level warehouse distribution node can only be supplied by a predetermined number of candidate sorting warehouses;

[0100] The distance between any subordinate warehouse distribution node and the candidate sorting warehouse it covers is less than the preset distance threshold;

[0101] The selected sorting bins include all existing sorting bins, and the required area of ​​any existing sorting bin for supplying goods to the next-level warehouse distribution node does not exceed the area of ​​the existing sorting bin;

[0102] The number of various vehicle models between any lower-level warehouse distribution node and the candidate sorting warehouse that does not supply it is 0;

[0103] The outbound quantity of the selected sorting bin is greater than 0, and the outbound quantity of the unselected sorting bin is equal to 0;

[0104] The demand area of ​​any selected sorting warehouse for supplying the next-level warehouse distribution node shall not exceed the sum of the basic warehouse area and the expansion warehouse area of ​​the selected sorting warehouse.

[0105] Specifically, the expressions of each constraint condition are as follows:

[0106] 1) Divide the target area into e zones, and select e warehouses from the candidate sorting warehouses;

[0107]

[0108] 2) If a candidate sorting warehouse is selected, it must deliver goods to at least one grid warehouse or group leader; unselected sorting warehouses cannot deliver goods to any grid warehouse or group leader;

[0109] for

[0110] Among them, δ is a small number, such as 0.1; ε is a large number, such as 10000, so that when candidate sorting bin i is not selected, Y i=0, the leftmost and rightmost sides of the inequality are equal to 0, which means that candidate sorting warehouse i cannot deliver goods to any grid warehouse or group leader.

[0111] 3) For grid warehouses and group leaders, they can only be supplied by one sorting warehouse;

[0112] for

[0113] for

[0114] 4) To ensure timeliness, the distance between the grid warehouse or the group leader and the warehouse it covers must be less than the preset distance D, for example, 200 km;

[0115] d ij *x ij ≤D; for

[0116] d ik *x ik ≤D; for

[0117] 5) An existing warehouse must be selected, and the area of ​​the existing warehouse must satisfy the constraints (assuming W is the set of existing warehouse numbers). For any w∈W, it satisfies:

[0118] Y w =1 (3.27)

[0119]

[0120] Among them, px1 is the level efficiency of the sorting warehouse to the grid warehouse, single / level; px2 is the level efficiency of the sorting warehouse to the group leader, single / level; EA w is the area of ​​the existing warehouse w; pn is the number of commodity types. Formula 3.28 indicates that for any existing sorting warehouse w, the required area for supplying grid warehouses and group leaders does not exceed the area of ​​the existing sorting warehouse.

[0121] 6) If sorting warehouse i does not deliver to grid warehouse j (or group leader k), the number of each vehicle type between the two locations is 0;

[0122] for

[0123] for

[0124] Among them, vtn1 is the number of vehicle models sent from the sorting warehouse to the grid warehouse; vtn2 is the number of vehicle models sent from the sorting warehouse to the group leader.

[0125] 7) If a warehouse is not enabled, its outbound volume is 0; if a warehouse is enabled, its outbound volume is greater than 0. That is:

[0126]

[0127]

[0128] Among them, a q is the maximum loading order quantity of vehicle type q corresponding to transporting goods from the sorting warehouse to the grid warehouse; b q is the maximum loading order quantity of vehicle type q corresponding to transporting goods from the sorting warehouse to the group leader.

[0129] 8) Transportation costs from the sorting warehouse to the grid warehouse and transportation costs from the sorting warehouse to the group leader.

[0130] Z ijq *T1 ijq =PtG ijq ;for

[0131] Z ikq *T2 ikq =PtC ikq ;for

[0132] Among them, T1 ijq T2 is the transportation cost function (known function) generated by transporting a vehicle of model q from sorting bin i to grid bin j, which is related to the transportation distance and vehicle model and is usually a piecewise continuous function; ikq is the transportation cost function (known function) generated by using a vehicle of model q to transport from sorting warehouse i to group leader k, which is related to the transportation distance and model, and is usually a piecewise continuous function.

[0133] 9) Warehouse rental cost calculation logic: All commodities are divided into three categories: standard products, fresh products, and frozen products. The required area of ​​each sorting warehouse can be calculated based on the total order volume and average efficiency of each category of commodities that each sorting warehouse is responsible for. Formula (3.35) lists the required area of ​​a certain sorting warehouse i. However, due to the actual constraints of the minimum warehouse rental area and the minimum warehouse expansion area, the required area and the actual warehouse rental area should satisfy Formulas (3.36) and (3.37), where Formula (3.37) ensures that if a candidate sorting warehouse is not selected, the corresponding warehouse rental area is 0. It should be noted that for existing warehouses, their warehouse rental area is fixed, so the U corresponding to the existing warehouse rent is i That is also certain.

[0134]

[0135] A i ≤5000*Y i +2500*Ui (3.36)

[0136] δ*Y i ≤U i ≤ε*Y i (3.37)

[0137] Among them, δ is a smaller number, such as 0.1; ε is a larger number, such as 10000.

[0138] After determining the objective function and constraints, a mixed integer linear programming solver (such as CPLEX, Gurobi, SCIP, etc.) can be used to solve the objective function. Specifically, the decision variables, constraints, and objective function are written into the solver in sequence, and the optimal solution can be obtained after running. Finally, according to the decision variable Y corresponding to the optimal solution i The warehouse network fission (warehouse expansion) plan can be determined by taking values, and the remaining parameters can also be checked by taking values ​​of corresponding decision variables. In actual operation, the cost and coverage relationship of different warehouse selection numbers can be given, such as Figure 5a , Figure 5b , Figure 5c As shown, Figure 5a The diagram is a schematic diagram of the optimal sorting bin coverage relationship when the number of proposed partitions is e=2. The target area is divided into two partitions, that is, two sorting bins are determined from the candidate sorting bins, and which grid bins and group leaders the selected sorting bins supply. Figure 5b FIG5 is a schematic diagram of the optimal sorting bin coverage relationship when the number of proposed partitions is e=3, and FIG6 is a schematic diagram of the optimal sorting bin coverage relationship when the number of proposed partitions is e=4, which will not be repeated here. The decision maker can select the best warehouse network optimization solution from the above results.

[0139] Figure 6 1 is a structural diagram of a warehousing and distribution network optimization device according to an embodiment of the present invention. The warehousing and distribution network optimization device provided in this embodiment can execute the processing flow provided in the method embodiment, such as Figure 6 As shown, the warehousing and distribution network optimization device 600 includes: a candidate sorting warehouse acquisition module 601, a basic parameter acquisition module 602, a construction module 603, and a solution module 604.

[0140] A candidate sorting bin acquisition module 601 is used to acquire candidate sorting bins in a target area;

[0141] The basic parameter acquisition module 602 is used to acquire the relevant basic parameters in the warehousing and distribution network;

[0142] A construction module 603 is used to construct an objective function and constraint conditions of a warehousing and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, wherein the objective function is a total cost function of the warehousing and distribution network;

[0143] The solution module 604 is used to call the preset solution program interface, solve the objective function according to the constraint conditions, and determine the target candidate sorting warehouse selection plan, the target warehousing plan of the selected sorting warehouse, and the target distribution plan that minimize the total cost of the warehousing and distribution network, so as to optimize the warehousing and distribution network according to the target candidate sorting warehouse selection plan, the target warehousing plan of the selected sorting warehouse, and the target distribution plan.

[0144] Based on any of the above embodiments, the relevant basic parameters in the warehousing and distribution network include: the distance from each candidate sorting warehouse to the lower-level warehousing and distribution node, the order demand of each lower-level warehousing and distribution node, the calculation method of the transportation cost of various vehicle models, the warehouse rental cost per unit area, and the labor cost of a single order.

[0145] On the basis of any of the above embodiments, when constructing the objective function of the warehousing and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, the construction module 603 is used to:

[0146] Determine the storage cost function based on the order demand of each lower-level warehouse distribution node, the labor cost of a single order, and the warehouse rental cost per unit area;

[0147] Determine the transportation cost function based on the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle models;

[0148] The sum of the warehousing cost function and the transportation cost function is determined as the objective function of the warehousing and distribution network optimization model.

[0149] Based on any of the above embodiments, when determining the storage cost function according to the order requirements of each subordinate warehouse distribution node, the labor cost of a single order, and the warehouse rental cost per unit area, the construction module 603 is used to:

[0150] Determine the labor cost sub-function based on the order demand of each lower-level warehouse distribution node and the labor cost of a single order;

[0151] Determine the warehouse rental cost sub-function based on the warehouse rental cost per unit area and the basic warehouse area and warehouse expansion area;

[0152] Determine the material cost sub-function according to a preset multiple of the sum of the labor cost sub-function and the warehouse rental cost sub-function;

[0153] The sum of the labor cost sub-function, the warehouse rental cost sub-function and the material cost sub-function is determined as the storage cost function.

[0154] Based on any of the above embodiments, when determining the transportation cost function according to the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle models, the construction module 603 is used to:

[0155] According to the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle models, the transportation cost sub-function of a single vehicle of any vehicle model from any candidate sorting warehouse to any lower-level warehouse distribution node is determined;

[0156] According to the transportation cost sub-function, respectively determine the total transportation cost function for the same type of subordinate warehouse distribution nodes;

[0157] The sum of the total transportation cost functions for various types of lower-level warehouse and distribution nodes is determined as the transportation cost function.

[0158] Based on any of the above embodiments, the relevant basic parameters in the storage and distribution network further include: the warehouse area, warehouse efficiency, and the number of planned partitions of each existing sorting warehouse in each candidate sorting warehouse;

[0159] The constraints of the warehousing and distribution network optimization model include one or more of the following:

[0160] The number of selected sorting bins is equal to the number of proposed partitions;

[0161] The selected sorting warehouse supplies at least one lower-level warehouse distribution node, and the unselected sorting warehouse cannot supply any lower-level warehouse distribution node;

[0162] Any lower-level warehouse distribution node can only be supplied by a predetermined number of candidate sorting warehouses;

[0163] The distance between any subordinate warehouse distribution node and the candidate sorting warehouse it covers is less than the preset distance threshold;

[0164] The selected sorting bins include all existing sorting bins, and the required area of ​​any existing sorting bin for supplying goods to the next-level warehouse distribution node does not exceed the area of ​​the existing sorting bin;

[0165] The number of various vehicle models between any lower-level warehouse distribution node and the candidate sorting warehouse that does not supply it is 0;

[0166] The outbound quantity of the selected sorting bin is greater than 0, and the outbound quantity of the unselected sorting bin is equal to 0;

[0167] The demand area of ​​any selected sorting warehouse for supplying the next-level warehouse distribution node shall not exceed the sum of the basic warehouse area and the expansion warehouse area of ​​the selected sorting warehouse.

[0168] Based on any of the above embodiments, when acquiring candidate sorting bins in a target area, the candidate sorting bin acquiring module 601 is used to:

[0169] Determine the target area according to the location of the lower-level warehouse distribution node of the sorting warehouse;

[0170] The target area is divided into a plurality of grids, and the center point of each grid is determined as the position of the candidate sorting bin.

[0171] Based on any of the above embodiments, when the solution module 604 calls the preset solution program interface to solve the objective function according to the constraint conditions, it is used to:

[0172] The program interface of the mixed integer linear programming solver is called to solve the objective function according to the constraint conditions.

[0173] The warehousing and distribution network optimization device provided in the embodiment of the present invention can be specifically used to execute the method embodiments provided in the above-mentioned Figures 2-3, and the specific functions will not be repeated here.

[0174] The storage and distribution network optimization device provided by the embodiment of the present invention obtains the candidate sorting warehouses in the target area; obtains the relevant basic parameters in the storage and distribution network; constructs the objective function and constraint conditions of the storage and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, wherein the objective function is the total cost function of the storage and distribution network; calls the preset solver interface, solves the objective function according to the constraint conditions, and determines the target candidate sorting warehouse selection scheme that makes the total cost of the storage and distribution network the lowest, the target warehousing scheme of the selected sorting warehouse, and the target distribution scheme, so as to optimize the storage and distribution network according to the target candidate sorting warehouse selection scheme, the target warehousing scheme of the selected sorting warehouse, and the target distribution scheme. This embodiment constructs the objective function and constraint conditions of the total cost of the storage and distribution network, and calls the preset solver interface for solution, so as to quickly and accurately determine the target candidate sorting warehouse selection scheme that makes the total cost of the storage and distribution network the lowest, the target warehousing scheme of the selected sorting warehouse, and the target distribution scheme, thereby more scientifically guiding the optimization of the storage and distribution network.

[0175] Figure 7 The electronic device provided by the embodiment of the present invention can execute the processing flow provided by the embodiment of the storage and distribution network optimization method, such as Figure 7 As shown, the electronic device 70 includes a memory 71, a processor 72, and a computer program; wherein the computer program is stored in the memory 71 and is configured so that the processor 72 executes the storage distribution network optimization method described in the above embodiment. In addition, the electronic device 70 may also have a communication interface 73 for transmitting control instructions and / or data.

[0176] Figure 7The electronic device of the illustrated embodiment can be used to execute the technical solution of the above-mentioned warehousing and distribution network optimization method embodiment, and its implementation principle and technical effect are similar and will not be repeated here.

[0177] In addition, this embodiment further provides a computer-readable storage medium on which a computer program is stored. The computer program is executed by a processor to implement the method described in the above embodiment.

[0178] In addition, this embodiment further provides a computer program product, including a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.

[0179] In the several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0180] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0181] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0182] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0183] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0184] The above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the embodiments of the present invention are described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

[0185] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be regarded as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0186] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for optimizing a warehousing and distribution network, characterized in that: include: Acquire candidate sorting bins in the target area, wherein the candidate sorting bins include existing sorting bins and sorting bins to be constructed; Obtaining relevant basic parameters in the warehousing and distribution network; the relevant basic parameters in the warehousing and distribution network include: the distance from each candidate sorting warehouse to the lower-level warehousing and distribution node, the order demand of each lower-level warehousing and distribution node, the calculation method of the transportation cost of various vehicle models, the warehouse rental cost per unit area, and the labor cost of a single order; According to the candidate sorting warehouses and the basic parameters, construct the objective function and constraint conditions of the warehouse distribution network optimization model, wherein the objective function is the total cost function of the warehouse distribution network; Calling a preset solver interface, solving the objective function according to the constraint conditions, and determining a target candidate sorting warehouse selection scheme, a target warehouse construction scheme for the selected sorting warehouse, and a target distribution scheme that minimize the total cost of the warehouse distribution network, so as to optimize the warehouse distribution network according to the target candidate sorting warehouse selection scheme, the target warehouse construction scheme for the selected sorting warehouse, and the target distribution scheme; The step of obtaining candidate sorting bins in the target area includes: Determine the target area according to the location of the lower-level warehouse distribution node of the sorting warehouse; The target area is divided into a plurality of grids, and the center point of each grid is determined as the position of the candidate sorting bin.

2. The method according to claim 1, characterized in that The objective function of the storage and distribution network optimization model is constructed according to the candidate sorting warehouses and the basic parameters, including: Determine the storage cost function based on the order demand of each lower-level warehouse distribution node, the labor cost of a single order, and the warehouse rental cost per unit area; Determine the transportation cost function based on the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle models; The sum of the warehousing cost function and the transportation cost function is determined as the objective function of the warehousing and distribution network optimization model.

3. The method according to claim 2, characterized in that The storage cost function is determined according to the order requirements of each lower-level warehouse distribution node, the labor cost of a single order, and the warehouse rental cost per unit area, including: Determine the labor cost sub-function based on the order demand of each lower-level warehouse distribution node and the labor cost of a single order; Determine the warehouse rental cost sub-function based on the warehouse rental cost per unit area and the basic warehouse area and warehouse expansion area; Determine the material cost sub-function according to a preset multiple of the sum of the labor cost sub-function and the warehouse rental cost sub-function; The sum of the labor cost sub-function, the warehouse rental cost sub-function and the material cost sub-function is determined as the storage cost function.

4. The method according to claim 2 or 3, characterized in that: The transportation cost function is determined based on the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle models, including: According to the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle models, the transportation cost sub-function of a single vehicle of any vehicle model from any candidate sorting warehouse to any lower-level warehouse distribution node is determined; According to the transportation cost sub-function, respectively determine the total transportation cost sub-function for the same type of lower-level warehouse distribution nodes; The sum of the total transportation cost sub-functions for various types of lower-level warehouse and distribution nodes is determined as the transportation cost function.

5. The method according to claim 4, characterized in that The relevant basic parameters in the storage and distribution network also include: the warehouse area, warehouse efficiency, and the number of planned partitions of each existing sorting warehouse in each candidate sorting warehouse; The constraints of the warehousing and distribution network optimization model include one or more of the following: The number of selected sorting bins is equal to the number of proposed partitions; The selected sorting warehouse supplies at least one lower-level warehouse distribution node, and the unselected sorting warehouse cannot supply any lower-level warehouse distribution node; Any lower-level warehouse distribution node can only be supplied by a predetermined number of candidate sorting warehouses; The distance between any subordinate warehouse distribution node and the candidate sorting warehouse it covers is less than the preset distance threshold; The selected sorting bins include all existing sorting bins, and the required area of ​​any existing sorting bin for supplying goods to the next-level warehouse distribution node does not exceed the area of ​​the existing sorting bin; The number of various vehicle models between any lower-level warehouse distribution node and the candidate sorting warehouse that does not supply it is 0; The outbound quantity of the selected sorting bin is greater than 0, and the outbound quantity of the unselected sorting bin is equal to 0; The demand area of ​​any selected sorting warehouse for supplying the next-level warehouse distribution node shall not exceed the sum of the basic warehouse area and the expansion warehouse area of ​​the selected sorting warehouse.

6. The method according to claim 1 or 2, characterized in that: The calling of a preset solver interface to solve the objective function according to the constraint conditions includes: The program interface of the mixed integer linear programming solver is called to solve the objective function according to the constraint conditions.

7. A storage and distribution network optimization device, characterized in that: include: A candidate sorting bin acquisition module is used to acquire candidate sorting bins in a target area, wherein the candidate sorting bins include existing sorting bins and sorting bins to be constructed; A basic parameter acquisition module is used to acquire basic parameters related to the warehousing and distribution network; the basic parameters related to the warehousing and distribution network include: the distance from each candidate sorting warehouse to the lower-level warehousing and distribution node, the order demand of each lower-level warehousing and distribution node, the calculation method of the transportation cost of various vehicle models, the warehouse rental cost per unit area, and the labor cost of a single order; A construction module, used to construct an objective function and constraint conditions of a warehousing and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, wherein the objective function is a total cost function of the warehousing and distribution network; A solution module, used to call a preset solution program interface, solve the objective function according to the constraint conditions, determine the target candidate sorting warehouse selection scheme, the target warehouse construction scheme of the selected sorting warehouse, and the target distribution scheme that minimize the total cost of the warehouse distribution network, so as to optimize the warehouse distribution network according to the target candidate sorting warehouse selection scheme, the target warehouse construction scheme of the selected sorting warehouse, and the target distribution scheme; When acquiring candidate sorting bins in a target area, the candidate sorting bin acquisition module is used to: Determine the target area according to the location of the lower-level warehouse distribution node of the sorting warehouse; The target area is divided into a plurality of grids, and the center point of each grid is determined as the position of the candidate sorting bin.

8. The device according to claim 7, characterized in that When constructing the objective function of the storage and distribution network optimization model according to the candidate sorting warehouses and the basic parameters, the construction module is used to: Determine the storage cost function based on the order demand of each lower-level warehouse distribution node, the labor cost of a single order, and the warehouse rental cost per unit area; Determine the transportation cost function based on the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle models; The sum of the warehousing cost function and the transportation cost function is determined as the objective function of the warehousing and distribution network optimization model.

9. The device according to claim 8, characterized in that When determining the storage cost function according to the order requirements of each subordinate warehouse distribution node, the labor cost of a single order, and the warehouse rental cost per unit area, the construction module is used to: Determine the labor cost sub-function based on the order demand of each lower-level warehouse distribution node and the labor cost of a single order; Determine the warehouse rental cost sub-function based on the warehouse rental cost per unit area and the basic warehouse area and warehouse expansion area; Determine the material cost sub-function according to a preset multiple of the sum of the labor cost sub-function and the warehouse rental cost sub-function; The sum of the labor cost sub-function, the warehouse rental cost sub-function and the material cost sub-function is determined as the storage cost function.

10. The device according to claim 8 or 9, characterized in that The construction module is used to determine the transportation cost function according to the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle models: According to the distance from each candidate sorting warehouse to the lower-level warehouse distribution node and the calculation method of the transportation cost of various vehicle models, the transportation cost sub-function of a single vehicle of any vehicle model from any candidate sorting warehouse to any lower-level warehouse distribution node is determined; According to the transportation cost sub-function, respectively determine the total transportation cost sub-function for the same type of lower-level warehouse distribution nodes; The sum of the total transportation cost sub-functions for various types of lower-level warehouse and distribution nodes is determined as the transportation cost function.

11. The device according to claim 10, characterized in that The relevant basic parameters in the storage and distribution network also include: the warehouse area, warehouse efficiency, and the number of planned partitions of each existing sorting warehouse in each candidate sorting warehouse; The constraints of the warehousing and distribution network optimization model include one or more of the following: The number of selected sorting bins is equal to the number of proposed partitions; The selected sorting warehouse supplies at least one lower-level warehouse distribution node, and the unselected sorting warehouse cannot supply any lower-level warehouse distribution node; Any lower-level warehouse distribution node can only be supplied by a predetermined number of candidate sorting warehouses; The distance between any subordinate warehouse distribution node and the candidate sorting warehouse it covers is less than the preset distance threshold; The selected sorting bins include all existing sorting bins, and the required area of ​​any existing sorting bin for supplying goods to the next-level warehouse distribution node does not exceed the area of ​​the existing sorting bin; The number of various vehicle models between any lower-level warehouse distribution node and the candidate sorting warehouse that does not supply it is 0; The outbound quantity of the selected sorting bin is greater than 0, and the outbound quantity of the unselected sorting bin is equal to 0; The demand area of ​​any selected sorting warehouse for supplying the next-level warehouse distribution node shall not exceed the sum of the basic warehouse area and the expansion warehouse area of ​​the selected sorting warehouse.

12. The device according to claim 7 or 8, characterized in that When the solution module calls the preset solution program interface to solve the objective function according to the constraint conditions, it is used to: The program interface of the mixed integer linear programming solver is called to solve the objective function according to the constraint conditions.

13. An electronic device, characterized in that: include: at least one processor; and memory; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method according to any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the method according to any one of claims 1 to 6 is implemented.

15. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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