Supply chain network optimization method and device, computer device and storage medium

Abstract

The application provides a supply chain network optimization method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining supply chain network configuration data, wherein the supply chain network configuration data comprises candidate warehouse data, warehouse level quantity and optimization target information; constructing an initial supply chain network based on the candidate warehouse data and the warehouse level quantity, wherein the network nodes of the initial supply chain network are determined according to candidate warehouses in the candidate warehouse data; performing node state optimization and node relationship optimization on the initial supply chain network based on the optimization target information to obtain an optimized supply chain network; and taking the optimized supply chain network as a target supply chain network when the optimized supply chain network meets preset supply chain network indicators. The method can effectively improve the optimization efficiency of the supply chain network, and then obtain a high-quality supply chain network meeting user requirements.

Description

Technical Field

[0001] The present application relates to the field of warehousing and logistics technology, and specifically to a supply chain network optimization method, device, computer equipment and storage medium. Background Art

[0002] Warehousing services refer to a service in which a custodian stores goods delivered by a depositor and collects storage fees from the depositor. The scope of warehousing services should include various types of warehousing services, such as frozen warehousing and fresh food warehousing. Currently, with the rapid development of industries such as e-commerce and logistics, fierce market competition, and policy guidance, warehousing services are increasingly being used in various fields. When setting up warehousing services, various users generally require optimization of supply chain networks to continuously reduce operating costs.

[0003] However, existing warehouse network optimization methods mainly select the optimal warehouse network structure by enumerating the supply relationships between all customers and warehouses and comparing the evaluation function values ​​of the resulting warehouse network structures. This not only has an extremely high computational complexity, but also does not consider the service capability level of the outlets, resulting in the low practicality of the resulting warehouse network structure.

[0004] Therefore, the existing warehouse network optimization methods have the technical problem of low warehouse network optimization efficiency due to unreasonable optimization strategy settings. Summary of the Invention

[0005] Based on this, it is necessary to provide a supply chain network optimization method, device, computer equipment and storage medium to address the above technical problems, so as to improve the optimization strategy of the supply chain network, thereby improving the warehouse network optimization efficiency and obtaining a supply chain network that meets user needs for their use.

[0006] In a first aspect, the present application provides a supply chain network optimization method, comprising:

[0007] Obtaining supply chain network configuration data, wherein the supply chain network configuration data includes candidate warehouse data, the number of warehouse levels, and optimization target information;

[0008] Based on the candidate warehouse data and the number of warehouse levels, construct an initial supply chain network, wherein network nodes of the initial supply chain network are determined according to the candidate warehouses in the candidate warehouse data;

[0009] Based on the optimization target information, performing node state optimization and node relationship optimization on the initial supply chain network to obtain an optimized supply chain network;

[0010] When the optimized supply chain network meets the preset supply chain network indicators, the optimized supply chain network is used as the target supply chain network.

[0011] In some embodiments of the present application, constructing an initial supply chain network based on the candidate warehouse data and the number of warehouse levels includes: if the supply chain network configuration data also includes the number of network warehouses, then based on a preset first adaptation algorithm, analyzing the number of network warehouses, the candidate warehouse data and the number of warehouse levels to construct the initial supply chain network; if the supply chain network configuration data does not include the number of network warehouses, then based on a preset first adaptation algorithm, analyzing the candidate warehouse data and the number of warehouse levels to construct the initial supply chain network.

[0012] In some embodiments of the present application, based on the optimization target information, the node status optimization and node relationship optimization of the initial supply chain network are performed to obtain an optimized supply chain network, including: based on a preset perturbation strategy, the node status optimization of each target warehouse node in the initial supply chain network is performed to obtain a preliminary optimized supply chain network; based on the optimization target information, the node relationship optimization of the preliminary optimized supply chain network is performed to obtain an optimized supply chain network.

[0013] In some embodiments of the present application, the perturbation strategy is more than one perturbation strategy, the candidate warehouse data includes the number of candidate warehouses, and the node state optimization of each target warehouse node in the initial supply chain network is performed based on the preset perturbation strategy to obtain a preliminarily optimized supply chain network, including: if the supply chain network configuration data also includes the number of network warehouses, then based on the number of candidate warehouses and the number of network warehouses, a first usage ratio of each perturbation strategy is determined, and based on the first usage ratio, the node state optimization of each target warehouse node in the initial supply chain network is performed to obtain a preliminarily optimized supply chain network; if the supply chain network configuration data does not include the number of network warehouses, then based on the number of candidate warehouses, a second usage ratio of each perturbation strategy is determined, and based on the second usage ratio, the node state optimization of each target warehouse node in the initial supply chain network is performed to obtain a preliminarily optimized supply chain network.

[0014] In some embodiments of the present application, the node relationship optimization of the supply chain network after the preliminary optimization is performed based on the optimization target information to obtain the optimized supply chain network, including: determining target rule information based on the preset coverage rules corresponding to the optimization target information; based on the target rule information, the node relationship optimization of the supply chain network after the preliminary optimization is performed to obtain the optimized supply chain network; wherein the target rule information is any one of the first rule information, the second rule information and the third rule information.

[0015] In some embodiments of the present application, the supply chain network configuration data also includes constraint information, and when the optimized supply chain network meets the preset supply chain network indicators, the optimized supply chain network is used as the target supply chain network, including: constructing an objective function according to the optimization target information and the constraint information to obtain the supply chain network indicators; based on the objective function, obtaining the first function result corresponding to the optimized supply chain network, and obtaining the second function result corresponding to the initial supply chain network; when the preferred result between the first function result and the second function result is the first function result, it is determined that the optimized supply chain network meets the supply chain network indicators, and the optimized supply chain network is used as the target supply chain network.

[0016] In some embodiments of the present application, when the preferred result between the first function result and the second function result is the first function result, the optimized supply chain network is determined to meet the supply chain network indicators, and the optimized supply chain network is used as the target supply chain network, including: if the objective function is the first objective function or the second objective function, the minimum value of the first function result and the second function result is screened out as the preferred result; if the objective function is the third objective function, the maximum value of the first function result and the second function result is screened out as the preferred result; when the preferred result is the first function result, the optimized supply chain network is determined to meet the supply chain network indicators, and the optimized supply chain network is used as the target supply chain network.

[0017] In some embodiments of the present application, after performing node state optimization and node relationship optimization on the initial supply chain network based on the optimization target information to obtain the optimized supply chain network, it also includes: when the optimized supply chain network does not meet the objective function, obtaining the network optimization time, and / or obtaining the number of network optimization iterations, wherein the objective function is constructed based on the optimization target information; if the network optimization time is greater than or equal to a preset optimization time threshold, and / or the number of network optimization iterations is greater than or equal to a preset iteration threshold, then the initial supply chain network is used as the target supply chain network; if the network optimization time is less than the optimization time threshold, and the number of network optimization iterations is less than the iteration threshold, then repeatedly performing node state optimization and node relationship optimization operations on the optimized supply chain network until the repeatedly optimized supply chain network meets the objective function, and then the repeatedly optimized supply chain network is used as the target supply chain network.

[0018] In some embodiments of the present application, the supply chain network configuration data also includes order demand data. After the optimized supply chain network is used as the target supply chain network, it also includes: optimizing the target supply chain network based on a pre-stored replenishment cycle list to obtain an optimized target supply chain network; obtaining the total order demand corresponding to each valid warehouse node based on the order demand data, wherein each valid warehouse node is a warehouse node in the optimized target supply chain network; obtaining the periodic inventory corresponding to each valid warehouse node based on the total order demand, the preset replenishment cycle duration and the replenishment cycle list; obtaining the total inventory cost of each valid warehouse node based on the periodic inventory and the preset inventory unit price data; and using the optimized target supply chain network as the preferred supply chain network when the total inventory cost meets the preset optimization termination condition.

[0019] In a second aspect, the present application provides a supply chain network optimization device, comprising:

[0020] A data acquisition module is used to acquire supply chain network configuration data, wherein the supply chain network configuration data includes candidate warehouse data, the number of warehouse levels, and optimization target information;

[0021] a network construction module, configured to construct an initial supply chain network based on the candidate warehouse data and the number of warehouse levels, wherein the network nodes of the initial supply chain network are determined according to the candidate warehouses in the candidate warehouse data;

[0022] A network optimization module, configured to optimize the node states and node relationships of the initial supply chain network based on the optimization target information to obtain an optimized supply chain network;

[0023] The target acquisition module is used to use the optimized supply chain network as the target supply chain network when the optimized supply chain network meets the preset supply chain network indicators.

[0024] In a third aspect, the present application further provides a computer device, comprising:

[0025] one or more processors;

[0026] a memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the supply chain network optimization method.

[0027] In a fourth aspect, the present application also provides a computer-readable storage medium on which a computer program is stored, and the computer program is loaded by a processor to execute the steps in the supply chain network optimization method.

[0028] In a fifth aspect, embodiments of the present application provide a computer program product or computer program, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the supply chain network optimization method provided in the first aspect.

[0029] The above-mentioned supply chain network optimization method, device, computer equipment and storage medium, the server constructs an initial supply chain network by acquiring supply chain network configuration data containing candidate warehouse data, the number of warehouse levels and optimization target information, and then optimizes the node status and node relationship of the initial supply chain network to achieve the optimization of the initial supply chain network and the acquisition of the optimized network, and finally analyzes whether the optimized supply chain network meets the preset supply chain network indicators, so as to use the optimized supply chain network that meets the supply chain network indicators as the target supply chain network. Since the supply chain network optimization scheme proposed in this application is essentially a warehouse network optimization scheme with "warehouse level enumeration instead of supply relationship enumeration" as the core. Therefore, the adoption of this scheme can greatly reduce the search space in the network optimization process, thereby effectively improving the optimization efficiency of the supply chain network. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 Schematic diagram of a supply chain network optimization method in an embodiment of the present application;

[0032] Figure 2 is a flow chart of the supply chain network optimization method in an embodiment of the present application;

[0033] Figure 3 This is a schematic diagram of the supply chain network structure in an embodiment of the present application;

[0034] Figure 4 This is a schematic diagram of the preferred structure of the supply chain network in the embodiment of the present application;

[0035] Figure 5 This is another preferred structural diagram of the supply chain network in the embodiment of the present application;

[0036] Figure 6 This is a schematic diagram of a specific process of the supply chain network optimization method in an embodiment of the present application;

[0037] Figure 7 This is a schematic diagram of the structure of the supply chain network optimization device in an embodiment of the present application;

[0038] Figure 8 It is a structural diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0040] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0041] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0042] The embodiments of the present application provide a supply chain network optimization method, apparatus, computer equipment, and storage medium, which are described in detail below.

[0043] See Figure 1 , Figure 1This is a scenario diagram of the supply chain network optimization method provided in the present application, and the supply chain network optimization method can be applied to a supply chain network optimization system. The supply chain network optimization system includes a terminal 100 and a server 200. The terminal 100 can be a device that includes both receiving and transmitting hardware, that is, a device with receiving and transmitting hardware that can perform two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device with a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The terminal 100 can specifically be a desktop terminal or a mobile terminal, and the terminal 100 can specifically be one of a mobile phone, a tablet computer, a laptop computer, etc. The server 200 can be an independent server, or a server network or a server cluster composed of servers, which includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0044] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario applicable to the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or fewer computer devices as shown in Figure 1 Only one server 200 is shown in FIG. 1 . It is understandable that the supply chain network optimization system may also include one or more other servers, which are not specifically limited here. Figure 1 As shown, the supply chain network optimization system may also include a memory for storing data, such as logistics data, for example, various data of the logistics platform, such as logistics transportation information of logistics outlets such as transit yards, specifically, express information, delivery vehicle information and logistics outlet information.

[0045] It should be noted that Figure 1 The scenario diagram of the supply chain network optimization system shown is only an example. The supply chain network optimization system and scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not constitute a limitation on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the supply chain network optimization system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0046] See Figure 2 , the embodiment of the present application provides a supply chain network optimization method, which is mainly applied to the above Figure 1Taking the server 200 in FIG. 1 as an example, the method includes steps S201 to S204, which are specifically as follows:

[0047] S201, obtaining supply chain network configuration data, wherein the supply chain network configuration data includes candidate warehouse data, the number of warehouse levels, and optimization target information.

[0048] The supply chain network configuration data may refer to the demand data pre-configured in the server 200 before the user submits a supply chain network optimization request, including but not limited to: candidate warehouse data, the number of warehouse levels, and optimization target information.

[0049] Among them, candidate warehouse data may refer to available warehouse data, which includes but is not limited to: the unique identification of the candidate warehouse, the address information of the candidate warehouse, the warehouse name of the candidate warehouse, etc. For example, the candidate warehouse data includes the unique identification, address information and warehouse name of candidate warehouse A and candidate warehouses B to G.

[0050] The number of warehouse levels can be the number of warehouse levels used to define the final required supply chain network, and this number of warehouse levels does not include the number of levels of the network's first node (factory / supplier) and the network's last node (customer / distributor). That is, the number of warehouse levels specifically refers to the number of warehouse levels used between the network's first node and the network's last node. For example, if the number of warehouse levels is represented by N, N = 2, and the number of levels of the first and last nodes is "1," then the final required supply chain network has four levels of nodes: the first-level node "factory / supplier," the middle-level nodes "first-level warehouse" and "second-level warehouse," and the last-level node "customer / distributor."

[0051] The optimization target information may be a special attribute used to define the final supply chain network, including but not limited to: (1) minimizing the total cost; (2) minimizing the number of warehouses used; and (3) maximizing the timeliness achievement rate.

[0052] Specifically, before optimizing a supply chain network in response to a user-submitted supply chain network optimization request, server 200 must first construct a supply chain network that is available for optimization and substantially meets the user's needs, namely, the initial supply chain network mentioned below. Constructing the initial supply chain network requires obtaining and analyzing the supply chain network configuration data submitted by the user. Therefore, after receiving a user-submitted supply chain network optimization request through terminal 100, or reading a user-preset supply chain network optimization request at a user-preset time, server 200 can immediately respond to the request and obtain the supply chain network configuration data, so that the supply chain network configuration data can be used as a basis for subsequent analysis to achieve efficient optimization of the supply chain network.

[0053] Furthermore, the supply chain network configuration data obtained by the server 200 may be all the data preset by the user and stored in the corresponding database of the server 200. In this case, the server 200 can directly read the relevant data in the database to obtain the supply chain network configuration data submitted by the user. In addition, the supply chain network configuration data obtained by the server 200 may also be part of the data preset by the user and stored in the corresponding database of the server 200, as well as data that is not pre-stored in the database but can be obtained from other devices according to user prompts. In this case, the server 200 can directly read part of the relevant data in the database and, according to user prompts, such as the data storage location submitted by the user (such as device A), apply for the remaining relevant data from that location, and combine them to obtain the supply chain network configuration data required subsequently.

[0054] S202: constructing an initial supply chain network based on the candidate warehouse data and the number of warehouse levels, wherein the network nodes of the initial supply chain network are determined according to the candidate warehouses in the candidate warehouse data.

[0055] The initial supply chain network can be a node network adapted to the supply chain scenario, that is, the nodes constituting the network can be supply and demand objects in the supply chain field, such as factories, suppliers, warehouses, customers (individual customers or stores), and distributors. Factories or suppliers with the same attributes in the above objects must exist in the network, and customers or distributors with the same attributes must exist in the network. In the embodiments of the present application, if there is no special quantity specification, one node represents one object. For example, see Figure 3 , is a structural diagram of a supply chain network. The network structure of the initial supply chain network can be Figure 3 The structure shown may also be a structure with more or fewer warehouses than it uses, and / or more or fewer warehouse levels than it has, and / or more or fewer factories / suppliers than it has, and / or more or fewer customers / distributors than it has.

[0056] Specifically, after the server 200 obtains the supply chain network configuration data required for network optimization, it can extract the candidate warehouse data and the number of warehouse levels from the supply chain network configuration data, and then analyze the candidate warehouse data and the number of warehouse levels to quickly build an initial supply chain network that meets the constraints set by the user as much as possible. At the same time, the initial supply chain network constructed at the current moment may include a network that does not meet the constraints set by the user, that is, when the server 200 executes this step, it can generate an "infeasible solution" that does not meet all the constraints, and then optimize and screen out a "feasible solution" in the future, and feed it back to the user as the final supply chain network required by the user. The initial supply chain network construction steps and the initial supply chain network optimization steps involved in this embodiment will be described in detail below.

[0057] It should be noted that the constraints or constraint information involved in the embodiments of the present application may be network optimization constraints corresponding to the above-mentioned optimization target information, including but not limited to: (1) the transportation cost of the target supply chain network is lower than the transportation cost of other supply chain networks; (2) the number of warehouses used in the target supply chain network is lower than the number of warehouses used in other supply chain networks; (3) the timeliness achievement rate of the target supply chain network is higher than the timeliness achievement rate of other supply chain networks.

[0058] In one embodiment, this step includes: if the supply chain network configuration data also includes the number of network warehouses, then based on a preset first adaptation algorithm, the number of network warehouses, the candidate warehouse data and the number of warehouse levels are analyzed to construct an initial supply chain network; if the supply chain network configuration data does not include the number of network warehouses, then based on a preset first adaptation algorithm, the candidate warehouse data and the number of warehouse levels are analyzed to construct an initial supply chain network.

[0059] The network warehouse quantity can be used to limit the number of warehouses included in the final supply chain network, including but not limited to: the total number of network warehouses and / or the total number of network-level warehouses. For example, if the network warehouse quantity refers only to the total number of network warehouses, a network warehouse quantity of "10" indicates that the total number of warehouses used by all warehouse levels in the corresponding supply chain network is "10." For another example, if the network warehouse quantity refers only to the total number of network-level warehouses, a network warehouse quantity of "10, 5, 2" indicates that the total number of first-tier warehouses (first-tier warehouses) in the corresponding supply chain network is "10," the total number of second-tier warehouses (second-tier warehouses) is "5," and the total number of third-tier warehouses (third-tier warehouses) is "2." In this case, the supply chain network has three tiers of warehouses, and the supply and demand relationship among these three tiers is: "factory / supplier" supplies first-tier warehouses, first-tier warehouses supply second-tier warehouses, second-tier warehouses supply third-tier warehouses, and third-tier warehouses supply "customers / distributors."

[0060] The first fit algorithm is also called the First Fit algorithm, which starts searching from the head of the free partition chain until a free partition that meets its size requirement is found.

[0061] Specifically, the FirstFit algorithm is used to construct the initial supply chain network, that is, under the constraints of the number of network warehouses and the number of warehouse levels, all candidate warehouses are assigned the first suitable state among all optional states one by one until all candidate warehouses obtain the state. For example, in a scenario without considering other constraints, the user wants a supply chain network with 3 first-level warehouses, that is, the first-level warehouse consists of three warehouses, then the three candidate warehouses initially analyzed will be set to "first-level warehouse state", if the number of warehouse levels N is 1, then the other candidate warehouses analyzed subsequently will be assigned to "not selected state", and an initial supply chain network containing three layers of nodes (factory / supplier node, first-level warehouse node, customer / distributor node) will be obtained. The state assigned by the server 200 to each candidate warehouse can be registered based on the candidate warehouse identifier contained in the candidate warehouse data.

[0062] It should be noted that the warehouse status involved in the embodiments of this application generally includes "selected status" and "not selected status". The "selected status" is set to "first-level warehouse status", "second-level warehouse status", "third-level warehouse status", etc. for use according to the number of warehouse levels. For example, when the number of warehouse levels N = 2, the range of selectable statuses corresponding to each candidate warehouse is [first-level warehouse, second-level warehouse, not selected]; when the number of warehouse levels N = 3, the range of selectable statuses corresponding to each candidate warehouse is [first-level warehouse, second-level warehouse, third-level warehouse, not selected], and so on.

[0063] More specifically, server 200 uses the First Fit algorithm to assign initial states to all candidate warehouses. For example, after analyzing a candidate warehouse and determining whether it will be used as a "first-level warehouse," "second-level warehouse," "third-level warehouse," or "not selected," server 200 is required to specify allocation and distribution warehouse coverage relationships for candidate warehouses other than those in the "not selected" state based on preset coverage rules. This includes specifying the replenishment coverage relationship from "upper-level warehouse to lower-level warehouse" and the supply coverage relationship from "warehouse to end-customer / supplier node." Once the coverage relationships are established, the initial supply chain network can be obtained, which is the current solution (S) mentioned in the embodiments of this application.

[0064] Furthermore, the above embodiment illustrates the situation when the supply chain network configuration data also includes the number of network warehouses used. If the supply chain network configuration data does not include the number of network warehouses used, it means that the server 200 cannot know the user-specified warehouse quantity requirements when constructing the initial supply chain network. Therefore, at this time, the server 200 can only assign status information to the candidate warehouse identifier in the candidate warehouse data based on the number of warehouse levels under the constraints of the optimization target information and constraint information, so as to construct an initial supply chain network with a quality that is not as good as the known network warehouse quantity. When the network is optimized subsequently, minimizing the number of warehouses used is used as one of the final optimization goals to achieve efficient optimization of the supply chain network.

[0065] S203 , based on the optimization target information, performing node state optimization and node relationship optimization on the initial supply chain network to obtain an optimized supply chain network.

[0066] Among them, node status optimization can refer to the status update operation mentioned above on whether the warehouse node is selected in the supply chain network, or which level of the warehouse level it is selected in. For example, the node status of warehouse A is optimized. The initial status of warehouse A is "secondary warehouse", and the optimized status of warehouse A is "primary warehouse".

[0067] Among them, node relationship optimization can refer to the operation of optimizing the supply and demand coverage relationship between each node in the supply chain network as mentioned above. For example, the initial node relationship of a supply chain network is that warehouse A (first-level warehouse) supplies warehouse C (second-level warehouse). After the server 200 optimizes the node relationship, the node relationship changes to warehouse B (first-level warehouse) supplies warehouse C (second-level warehouse). Warehouse A (first-level warehouse) may switch to supplying other lower-level warehouses, or switch to supplying end customers / distributor nodes.

[0068] Specifically, after server 200 analyzes the candidate warehouse data and the number of warehouse levels and constructs the initial supply chain network, it is necessary to optimize the node status of the initial supply chain network and the node relationship after the node status optimization based on the optimization target information contained in the supply chain network configuration data, in order to obtain the optimized supply chain network.

[0069] However, the supply chain network after completing the node status optimization and node relationship optimization, although the network structure attributes are more able to meet the actual needs of users than the initial supply chain network, for example, in terms of transportation costs, warehouse costs, and transportation time. However, the network that has completed the optimization at this stage cannot be directly output to users for use, because there is a high possibility that there are multiple networks, or the upper limit of the network attributes has not yet reached the lower limit of the user's demand, but is only close to the lower limit of the demand. Therefore, this application proposes to explain in detail in the following embodiments how to determine whether the optimized supply chain network has met the output standards.

[0070] In one embodiment, this step includes: based on a preset perturbation strategy, optimizing the node status of each target warehouse node in the initial supply chain network to obtain a preliminarily optimized supply chain network; based on the optimization target information, optimizing the node relationship of the preliminarily optimized supply chain network to obtain an optimized supply chain network.

[0071] Among them, the "disturbance" involved in the embodiment of the present application actually corresponds to the concept of neighborhood space search in the neighborhood search, that is, the hierarchical state of some target warehouses in the current solution (S) is modified to obtain a new solution (S') as the optimized supply chain network. For example, for a supply chain network configured as N layers, if there is no special limitation, the hierarchical state range that each candidate warehouse can choose from high to low levels is: "Level 1 warehouse" Level (1) to "Level N warehouse" Level (N), or "not selected" Level (-1), then the target warehouse node in the initial supply chain network can also select a state that can make the network quality higher based on this range.

[0072] Level (0) represents the "factory / supplier," which supplies only the "first-level warehouse." Level (N+1) represents the "customer / distributor," but customers / distributors can retrieve goods from any warehouse level between the "first-level warehouse" and the "N-level warehouse." Furthermore, the node status of Level (0) and Level (N+1) cannot be modified. This means that network nodes defined as "factory / supplier" or "customer / distributor" are mandatory nodes that cannot be planned.

[0073] The target warehouse node may be a candidate warehouse selected to construct the initial supply chain network.

[0074] Specifically, the operation of optimizing the initial supply chain network by server 200 is mainly divided into two aspects, namely, optimizing the node status of the target warehouse node, and optimizing the node relationship of each node in the initial supply chain network, including "factory / supplier" and "customer / distributor", etc. By adjusting the nodes used in the initial supply chain network, the hierarchical position of the nodes, and the supply and demand relationship of the nodes, an optimized supply chain network can be obtained.

[0075] For example, Figure 3 As shown in the figure, the server 200 implements a disturbance on the initial supply chain network, which can change the hierarchical status of the target warehouse w from "secondary warehouse" to "not selected", that is, change the attached attribute of the warehouse identifier corresponding to the target warehouse w from Level (2) to Level (-1). Figure 3 The last "first-level warehouse" has only one "second-level warehouse" left. The "distributors" and "e-commerce" that originally needed to pick up goods from the target warehouse w now have to pick up goods from the upper-level warehouse of the target warehouse w.

[0076] As a result, the relationship between the relevant nodes (straight lines with arrows) has also changed, and the object pointed by the relationship arrow is the party demanding the goods.

[0077] Of course, server 200's "perturbation" of the initial supply chain network is not limited to optimizing the node states of each target warehouse or candidate warehouse. It can also involve swapping the node states of warehouses w and v to analyze network quality. Therefore, perturbation strategies can be designed in different ways, but the overall goal of all strategies is to improve the quality of the resulting new solution (S') through perturbation. The node state optimization steps and node relationship optimization steps involved in this embodiment are described in detail below.

[0078] In one embodiment, the perturbation strategy is more than one perturbation strategy, and the candidate warehouse data includes the number of candidate warehouses. The above-mentioned step of optimizing the node status of each target warehouse node in the initial supply chain network based on the preset perturbation strategy to obtain a preliminarily optimized supply chain network includes: if the supply chain network configuration data also includes the number of network warehouses, then based on the number of candidate warehouses and the number of network warehouses, determining the first usage ratio of each perturbation strategy, and based on the first usage ratio, optimizing the node status of each target warehouse node in the initial supply chain network to obtain a preliminarily optimized supply chain network; if the supply chain network configuration data does not include the number of network warehouses, then based on the number of candidate warehouses, determining the second usage ratio of each perturbation strategy, and based on the second usage ratio, optimizing the node status of each target warehouse node in the initial supply chain network to obtain a preliminarily optimized supply chain network.

[0079] The multiple perturbation strategies proposed in this embodiment include, but are not limited to, the following four strategies: (1) changing the node status of one warehouse; (2) exchanging the node status of two warehouses; (3) changing the node status of one group (1 to 3) of warehouses; and (4) exchanging the status of two groups (1 to 3) of warehouses. The above-mentioned warehouses include the target warehouses that have been used and the candidate warehouses that have not yet been used.

[0080] The number of candidate warehouses may refer to the number of candidate warehouses available for selection to construct a supply chain network. For example, the number of candidate warehouses is "15", which means that there are 15 candidate warehouses available for selection to construct a supply chain network.

[0081] Among them, the number of network storage bins has been explained in detail in the above embodiments. Please refer to the above embodiments for details and will not be repeated here.

[0082] Specifically, before perturbing the initial supply chain network, server 200 must analyze the supply chain network configuration data submitted by users in order to optimize the initial supply chain network in a targeted manner based on the user needs expressed in the supply chain network configuration data. Based on this, the present embodiment proposes optimizing the initial supply chain network by determining whether the supply chain network configuration data contains the number of network warehouses and selecting an appropriate perturbation strategy.

[0083] For example, if the supply chain network configuration data also includes the number of network warehouses, the server 200 can analyze the size of the number of network warehouses and the number of candidate warehouses, calculate the usage ratio of the above four perturbation strategies, and obtain a first usage ratio, so as to use the first usage ratio to optimize the node status of each target warehouse node in the initial supply chain network: (1) When the number of candidate warehouses is no more than 3 more than the number of network warehouses, strategy 1 accounts for 40%, strategy 2 accounts for 50%, strategy 3 accounts for 5%, and strategy 4 accounts for 5%, and the number of warehouses in the group involved is "1 to 3"; (2) When the number of candidate warehouses is less than 25, strategy 1 accounts for 30%, strategy 2 accounts for 50%, strategy 3 accounts for 10%, and strategy 4 accounts for 10%, and the number of warehouses in the group involved is "1 to 3". The above-mentioned quantity thresholds such as "3" and "25" and the number of warehouses in the group can be determined according to application requirements and are not specifically limited in this application.

[0084] For another example, if the supply chain network configuration data does not include the number of network warehouses, the server 200 cannot analyze the number of network warehouses, but can only analyze the range of the number of candidate warehouses to which it belongs, thereby calculating the usage ratio of the above four perturbation strategies, obtaining the second usage ratio, and then using the second usage ratio to optimize the node status of each target warehouse node in the initial supply chain network: (1) When the number of candidate warehouses is less than 10, strategy 1 accounts for 50%, strategy 2 accounts for 30%, strategy 3 accounts for 10%, and strategy 4 accounts for 10%, involving the number of warehouses in the group of "1 to 3"; (2) When the number of candidate warehouses is greater than 100, strategy 1 accounts for 30%, strategy 2 accounts for 10%, strategy 3 accounts for 30%, and strategy 4 accounts for 30%, involving the number of warehouses in the group of "1 to 6". The above-mentioned quantity thresholds such as "10" and "100" and the number of warehouses in the group can be determined according to application requirements and are not specifically limited in this application.

[0085] In one embodiment, the above-mentioned step of optimizing the node relationships of the supply chain network after the preliminary optimization based on the optimization target information to obtain the optimized supply chain network includes: determining target rule information based on the preset coverage rules corresponding to the optimization target information; optimizing the node relationships of the supply chain network after the preliminary optimization based on the target rule information to obtain the optimized supply chain network; wherein the target rule information is any one of the first rule information, the second rule information and the third rule information.

[0086] The coverage rules have been explained in detail in the above examples. They refer to the supply and demand relationships between nodes, also known as "replenishment / supply relationships." For example, a "factory" replenishes a "first-level warehouse," a higher-level warehouse replenishes a lower-level warehouse, and a warehouse supplies a terminal "customer."

[0087] The target rule information may refer to the currently selected coverage rule to be executed, and the coverage rules include but are not limited to: (1) the rule of lowest transportation cost; (2) the rule of lowest number of warehouses used; and (3) the rule of highest timeliness achievement rate.

[0088] Specifically, the server 200 can analyze the coverage rules corresponding to the optimization target information contained in the supply chain network configuration data, and first determine the target rule information to be used, that is, when the coverage rule corresponding to the optimization target information is the first target information, the first target information is determined to be the target rule information, and then the first rule information is used to optimize the node relationship of each network node in the initially optimized supply chain network to obtain the optimized supply chain network; when the coverage rule corresponding to the optimization target information is the second target information, the second target information is determined to be the target rule information, and then the second rule information is used to optimize the node relationship of each network node in the initially optimized supply chain network to obtain the optimized supply chain network; when the coverage rule corresponding to the optimization target information is the third target information, the third target information is determined to be the target rule information, and then the third rule information is used to optimize the node relationship of each network node in the initially optimized supply chain network to obtain the optimized supply chain network.

[0089] For example, if the user presets the first rule information (1) of the lowest transportation cost rule, then for a "first-level warehouse A", the transportation costs of all available "factories" to "first-level warehouse A" will be calculated first, and then the "factory" with the lowest transportation cost will be selected as A's replenishment factory.

[0090] For another example, if the user presets the first rule information (1) of the lowest transportation cost rule, then for a "secondary warehouse B", the transportation costs from all "first-level warehouses" to "secondary warehouse B" will be calculated first, and then the "first-level warehouse" with the lowest transportation cost will be selected as B's replenishment warehouse.

[0091] Therefore, when the user presets the processing mechanism for selecting the second rule information (2) using the minimum number of warehouses, the server 200 can refer to the operating mechanism when selecting the first rule information (1) to perform the corresponding operation. However, it should be noted that the calculation of transportation costs and timeliness achievement rate requires the use of the address information of each candidate warehouse, as well as the transportation time and transportation distance between each address. Therefore, if the server 200 has initially detected that the optimization target information submitted by the user is the above-mentioned covering rule (1) or (3), the server 200 also needs to analyze whether the user has submitted the candidate warehouse address and logistics capacity information (the speed and working hours of the transportation capacity such as cars, airplanes, and ships). If submitted at the same time, a prompt message needs to be generated to prompt the user to bind the submission so that the transportation cost and timeliness achievement rate can be smoothly calculated later.

[0092] S204: When the optimized supply chain network meets the preset supply chain network indicators, the optimized supply chain network is used as the target supply chain network.

[0093] The preset supply chain network indicator may be a preset indicator represented as an objective function, and the objective function may be a function determined according to constraint information and optimization target information.

[0094] Specifically, the supply chain network quality judgment method proposed in this embodiment can be completed with the help of one objective function or multiple objective functions. However, if multiple objective functions are used, the user needs to specify the priority of each objective function.

[0095] More specifically, after the server 200 obtains the objective function, it will determine whether the optimized supply chain network satisfies the corresponding objective function or multiple objective functions. If all of them are satisfied, the optimized supply chain network can be used as the target supply chain network and output to the terminal 100 for display for user use. In addition, to determine whether the optimized supply chain network satisfies the corresponding objective function, it is mainly based on the calculated objective function value, that is, after obtaining the objective function value corresponding to each supply chain network in the objective function, the objective function value corresponding to each supply chain network is compared. Based on the comparison result of the objective function value, it can be determined whether the optimized supply chain network satisfies the corresponding objective function. The judgment steps involved in this embodiment will be described in detail below.

[0096] In one embodiment, the supply chain network configuration data also includes constraint information. This step includes: constructing an objective function based on the optimization target information and the constraint information to obtain the supply chain network indicators; based on the objective function, obtaining the first function result corresponding to the optimized supply chain network, and obtaining the second function result corresponding to the initial supply chain network; when the preferred result between the first function result and the second function result is the first function result, determining that the optimized supply chain network meets the supply chain network indicators, and using the optimized supply chain network as the target supply chain network.

[0097] Specifically, the objective function involved in this embodiment is mainly set based on optimization target information and constraint information, and includes at least the following three types:

[0098] (1) The lowest transportation cost. The transportation cost for a flow (i.e., a supply coverage relationship mentioned above) is calculated using the following formula:

[0099] Cost=Cargo Amount*Distance*Price

[0100] Among them, "Cargo Amount" is the amount of cargo to be transported. The cargo amount here can be calculated using piece quantity, weight or volume as the unit, which is configured by the user; "Distance" is the transportation distance from the starting point to the destination point in a pair of supply coverage relationships, which is obtained in advance by calling the geographic information system (GIS) before running the algorithm; "Price" is the transportation price per unit of cargo per kilometer, which is configured by the user.

[0101] (2) Minimum number of warehouses used. When the user does not specify the number of warehouses used in the final network structure (that is, when there is no constraint on the number of warehouses used in the network), "minimum number of warehouses used" can be used as one of the optimization objectives. The "number of warehouses used" can be obtained by calculating the number of "non-unselected warehouses" among all candidate warehouses, that is, the number of warehouses whose status is not "Level (-1)".

[0102] (3) Highest timeliness achievement rate. The data input by the user may require that the transportation time from the warehouse to each customer point must be within a specified time (called "timeliness requirement"). If the data is obtained based on the GIS system and the transportation time of a covering relationship flow direction is calculated to meet the timeliness requirement, it means that the timeliness requirement is "met". Otherwise, if the transportation time exceeds the timeliness requirement, it means that it is "not met". "Highest timeliness achievement rate for all customers" can also be used as one of the optimization goals.

[0103] More specifically, the first function result and the second function result in this embodiment are the objective function values ​​mentioned in the previous embodiment, i.e., the numerical results obtained by analyzing and optimizing the supply chain network using the objective function. Comparing the numerical results of two supply chain networks based on the same objective function provides a basis for determining whether one supply chain network, i.e., the optimized supply chain network, satisfies the objective function. If so, the target supply chain network is obtained. Based on this, the comparison of the numerical results determines whether the optimized supply chain network satisfies the objective function, as explained in detail below.

[0104] In one embodiment, the above-mentioned step of determining that the optimized supply chain network meets the supply chain network indicators when the preferred result between the first function result and the second function result is the first function result, and using the optimized supply chain network as the target supply chain network includes: if the objective function is the first objective function or the second objective function, screening out the minimum value of the first function result and the second function result as the preferred result; if the objective function is the third objective function, screening out the maximum value of the first function result and the second function result as the preferred result; when the preferred result is the first function result, determining that the optimized supply chain network meets the supply chain network indicators, and using the optimized supply chain network as the target supply chain network.

[0105] Among them, the first objective function can be the "lowest transportation cost" mentioned in the above embodiment, the second objective function can be the "minimum number of warehouses" mentioned in the above embodiment, and the third objective function can be the "highest timeliness achievement rate" mentioned in the above embodiment.

[0106] Specifically, the preferred results proposed in the embodiments of the present application are the results of determinations corresponding to different objective functions. For example, if the corresponding objective function is the result of the first function, the preferred result should be "minimum transportation cost"; if the corresponding objective function is the result of the second function, the preferred result should be "minimum number of warehouses used"; and if the corresponding objective function is the result of the third function, the preferred result should be "maximum timeliness achievement rate." Therefore, in the process of analyzing and screening the preferred results, the server 200 needs to select based on the setting of the objective function. That is, when the objective function is set to the first objective function or the second objective function, the minimum result between the first function result and the second function result needs to be screened out as the preferred result; when the objective function is set to the third objective function, the maximum result between the first function result and the second function result needs to be screened out as the preferred result.

[0107] For example, when the goal is "minimum transportation cost", the solution with a lower calculated cost value can be evaluated as a "better solution". If the "better solution" is actually an optimized supply chain network, the optimized supply chain network can be used as the target supply chain network. Otherwise, another network can be used as the target supply chain network.

[0108] For example, when the goal is to "minimize the number of warehouses used", the solution with fewer warehouses used can be evaluated as a "better solution". If the "better solution" is actually the optimized supply chain network, the optimized supply chain network can be used as the target supply chain network. Otherwise, another network can be used as the target supply chain network.

[0109] For another example, when the goal is "the highest on-time achievement rate", the solution with the highest calculated on-time achievement rate can be evaluated as the "better solution". If the "better solution" is actually the optimized supply chain network, the optimized supply chain network can be used as the target supply chain network. Otherwise, another network can be used as the target supply chain network.

[0110] In addition, although the solutions illustrated in the above embodiments are all based on the case where the preferred result is the first function result corresponding to the optimized supply chain network, when the preferred result is the second function result, strategies such as simulated annealing algorithm or taboo search algorithm can also be used to analyze whether the optimized supply chain network should be used as the target supply chain network to avoid the situation where the algorithm search process is limited to the local optimum, thereby improving the efficiency of searching for the global optimal solution, that is, improving the optimization efficiency of the supply chain network.

[0111] For example, the simulated annealing algorithm allows, during the optimization iteration process, to temporarily accept some suboptimal solutions (S') with a certain probability (P) as target solutions, and then perturb the target solution to continue the subsequent optimization iteration process. In other words, during the search process, it is allowed to perturb some "worse solutions" in the hope of obtaining better solutions, rather than just perturbing the best solution currently found. Therefore, in the simulated annealing algorithm, a solution that is better than the current best solution will definitely become the target solution, but a solution that is worse than the current best solution still has a certain probability (P) of becoming the target solution. This process of P value decreasing from large to small can be defined by the user, for example, the P value decreases by 0.1% every 1000 iterations.

[0112] For example, during the search process, tabu search records the most recently used perturbation strategies and perturbation objects and stores them in a tabu list. Each iteration ensures that objects in the tabu list are not used. This means that recently perturbed objects are not perturbed during the iterative search process, significantly improving the search efficiency for the target solution and, in turn, the optimization efficiency of the supply chain network.

[0113] In one embodiment, after this step, it also includes: when the optimized supply chain network does not meet the objective function, obtaining the network optimization time, and / or obtaining the number of network optimization iterations, wherein the objective function is constructed according to the optimization target information; if the network optimization time is greater than or equal to the preset optimization time threshold, and / or the number of network optimization iterations is greater than or equal to the preset iteration threshold, then the initial supply chain network is used as the target supply chain network; if the network optimization time is less than the optimization time threshold, and the number of network optimization iterations is less than the iteration threshold, then repeatedly performing node status optimization and node relationship optimization operations on the optimized supply chain network until the repeatedly optimized supply chain network meets the objective function, and then the repeatedly optimized supply chain network is used as the target supply chain network.

[0114] The network optimization duration can refer to the total execution time of the supply chain network optimization method, or the cumulative time from the most recent moment in the execution of the method until a better solution (i.e., a better supply chain network) is found, for example, 1 minute, 1 hour, etc.

[0115] The number of network optimization iterations can refer to the total number of iterations executed by the supply chain network optimization method, or the cumulative number of iterations since the most recent moment in the execution of the method before a more optimal solution (i.e., a more optimal supply chain network) is found, for example, 2, 10, etc.

[0116] The optimization duration threshold may be a critical value used to analyze whether the network optimization duration has reached a user-set limit, and may correspond to the "total duration" or the "accumulated duration" mentioned above, for example, 5 minutes or 30 minutes.

[0117] The iteration number threshold can be a critical value for analyzing whether the number of network optimization iterations has reached the limit set by the user. It can correspond to the "total number of iterations" mentioned above or the "accumulated number of iterations" mentioned above, for example, 5 times, 10 times, etc.

[0118] Specifically, the construction and optimization of the supply chain network by the server 200 should not be endless, especially for the optimization of the supply chain network. If the optimization operation is continuously implemented and a better solution is still not obtained, the optimization efficiency of the supply chain network is likely to be reduced. Therefore, in order to improve the optimization efficiency of the supply chain network, in addition to proposing the efficiency improvement scheme involved in the above embodiment, this application also proposes to improve efficiency by setting a threshold in this embodiment, that is, shortening the time for the server 200 to output a "better solution" so that it can output the supply chain network with the best quality within a period of time, thereby realizing the user's demand for obtaining the preferred supply chain network.

[0119] More specifically, this embodiment proposes to analyze the duration and / or number of iterations of the server 200 optimizing the supply chain network by obtaining the network optimization duration and / or the number of network optimization iterations. If any one of the duration and the number of iterations exceeds the user-preset threshold, it indicates that the server 200 has not found a better solution than the initial solution "initial supply chain network" within the period of time set by the user. At this time, the "initial supply chain network" can only be output as the target supply chain network.

[0120] Furthermore, if the duration and number of iterations do not exceed the user-preset threshold, it indicates that the duration of the server 200 optimizing the supply chain network has not reached the duration set by the user, and the optimization operation can continue to be performed. Therefore, the node status optimization and node relationship optimization operations of the optimized supply chain network can be repeatedly performed until the repeatedly optimized supply chain network meets the objective function. The repeatedly optimized supply chain network can then be output as the target supply chain network.

[0121] In one embodiment, the supply chain network configuration data also includes order demand data. After this step, it also includes: optimizing the target supply chain network based on a pre-stored replenishment cycle list to obtain an optimized target supply chain network; obtaining the total order demand corresponding to each valid warehouse node based on the order demand data, wherein each valid warehouse node is a warehouse node in the optimized target supply chain network; obtaining the periodic inventory corresponding to each valid warehouse node based on the total order demand, the preset replenishment cycle duration and the replenishment cycle list; obtaining the total inventory cost of each valid warehouse node based on the periodic inventory and the preset inventory unit price data; and selecting the optimized target supply chain network as the preferred supply chain network when the total inventory cost meets the preset optimization termination condition.

[0122] The replenishment cycle list can be a dynamic list (L) used to manage the replenishment cycles between nodes in the supply chain network. The replenishment cycle refers to the waiting time between two replenishment time points. For example, a replenishment cycle of "30 days" means that a replenishment action is completed every 30 days between two node objects.

[0123] The order demand data may include information such as order category, order quantity, order acquisition volume, and order timeliness requirements.

[0124] Among them, the cycle inventory can be expressed as (W), the preset replenishment cycle length can be expressed as (T), the total order demand can be expressed as (D), and the preset inventory unit price data can be expressed as (O).

[0125] Specifically, the server 200 constructs an initial supply chain network in response to the user's request and optimizes the initial supply chain network. After obtaining the target supply chain network, a network structure with node information and basic relationships between nodes is obtained. However, in the field of logistics supply chain, it is also necessary to respond to user needs and set a replenishment cycle based on the basic relationship between nodes in order to obtain the replenishment relationship between nodes. Therefore, after obtaining the target supply chain network, the server 200 can use the replenishment cycle table pre-stored by the user to optimize the target supply chain network in order to obtain the following: Figure 4 The optimized target supply chain network is shown.

[0126] For example, see Figure 4 Server 200 can use a dynamic list (L) to manage the replenishment cycle parameters between nodes in the network. (L) stores the time (e.g., in days) when replenishment requests are initiated by warehouses at all levels and end customers from top to bottom. Therefore, in a network with N warehouses, the length of L should be N+1. That is, in a supply chain network with N=2, the replenishment cycle list L=45, 30, 7 indicates that the "first-level warehouse" initiates a replenishment order to the "factory / supplier" every 45 days, the "second-level warehouse" initiates a replenishment order to the "first-level warehouse" every 30 days, and the "customer / distributor" initiates a replenishment order to the corresponding warehouse ("first-level warehouse" or "second-level warehouse") every 7 days.

[0127] In addition, the server 200 optimizes the target supply chain network based on the replenishment cycle to obtain an optimized target supply chain network. The optimized target supply chain network can be further iteratively optimized, that is, the node state optimization and node relationship optimization of the optimized target supply chain network can be performed using the solution described in the above embodiment to save the number of network nodes and thus achieve cost optimization of the network. For example, Figure 5 The network structure shown is Figure 4 Another optimized form of the network structure. Compared with the previous network structure, the current network structure omits the "secondary warehouse".

[0128] Furthermore, the replenishment cycle value determines the warehouse's inventory level, thus affecting the warehouse's inventory cost, and thus the operating cost of the entire supply chain network, becoming an important factor in evaluating the quality of a supply chain network. Therefore, the embodiment of the present application not only proposes a solution for optimizing the supply chain network through the replenishment cycle table, but also proposes a supply chain network optimization analysis solution based on this, so as to determine whether the target supply chain network with replenishment cycle information meets the user's actual needs, and then output the supply chain network containing replenishment cycle information as the preferred supply chain network for user use.

[0129] Furthermore, the supply chain network optimization analysis scheme proposed in the embodiment of the present application means that for a supply chain network, the cycle inventory (W) of each warehouse is equal to the sum of the demand of the end customers it covers and the inventory of the next-level warehouse. When calculating the inventory cost of each warehouse in each network level, it is necessary to consider not only the replenishment cycle of the corresponding warehouse, but also the replenishment cycle duration of the corresponding warehouse, so as to obtain the cycle inventory (W) of the warehouse under the replenishment cycle duration, and then combine the cycle inventory (W) and the inventory unit price data (O) to calculate the total inventory cost of the warehouse.

[0130] Finally, the total inventory cost of the warehouses used in each network layer is summed up to obtain the total inventory cost of the supply chain network. Then, the total inventory cost of the supply chain network is analyzed to see whether it meets the preset optimization termination condition. For example, if the total inventory cost is lower than the preset cost threshold, the supply chain network that meets the optimization termination condition can be selected as the preferred supply chain network for user use. The optimization termination condition involved in this embodiment can also be used as Figure 6 The "termination judgment" condition in the flowchart of the solution shown can be adjusted by the user based on actual application needs. Figure 6 The details of each step in the flowchart of the solution shown, such as the addition or removal of disturbance strategies, the addition or removal of network quality assessment criteria, and the addition or removal of network optimization termination conditions.

[0131] For example, there is a supply chain network with two warehouses (N=2) and a replenishment cycle list (L=90, 30, 7). Therefore, the "first-level warehouse" sends a replenishment request to the "factory / supplier" every 90 days (L(1)=90); the "second-level warehouse" sends a replenishment request to the "first-level warehouse" every 30 days (L(2)=30). The "customer / distributor" sends a supply request to the "first-level warehouse" or "second-level warehouse" every 7 days (L(3)=7), and the user-preset replenishment cycle duration T=365 (days).

[0132] Then, if a certain secondary warehouse needs to be analyzed, and based on the above analysis, it is concluded that it covers 10 customer nodes, that is, it needs to supply goods to these 10 "customers", and the total order demand of these 10 customers for 365 days is 10,000 units of goods, that is, D = 10,000, and the cycle inventory of the secondary warehouse is W = D / (T / L(2)), then W = 10,000 / (365 / 30) = 822. Therefore, if the inventory unit price data O = 20, the total inventory cost of the secondary warehouse Q = W*O = 822*20 = 16440. Finally, the server 200 can obtain the total inventory cost corresponding to each warehouse in the network in this way, calculate the total inventory cost of the supply chain network, and analyze the size of the total inventory cost and the preset cost threshold to determine whether the supply chain network is used as the preferred supply chain network. Therefore, the embodiment of the present application proposes a solution for optimizing the supply chain network by setting a replenishment cycle and utilizing inventory costs. This solution further improves the optimization details of the supply chain network on the basis of the solution described in the above embodiment, so that the quality of the final preferred supply chain network is more in line with user needs.

[0133] In the supply chain network optimization method in the above embodiment, the server constructs an initial supply chain network by acquiring supply chain network configuration data including candidate warehouse data, the number of warehouse levels, and optimization target information, and then optimizes the node status and node relationship of the initial supply chain network to achieve the optimization of the initial supply chain network and the acquisition of the optimized network. Finally, it analyzes whether the optimized supply chain network satisfies the preset supply chain network indicators, so as to determine the optimized supply chain network that satisfies the supply chain network indicators as the target supply chain network. Since the supply chain network optimization scheme proposed in this application is essentially a warehouse network optimization scheme with "warehouse level enumeration instead of supply relationship enumeration" as the core. At the same time, it is also proposed to adopt a neighborhood search method to obtain an acceptable approximate optimal solution within an appropriate time, thereby avoiding the long computational waiting time for solving the optimal solution. Therefore, the adoption of this scheme can greatly reduce the search space and search time in the network optimization process, thereby effectively improving the optimization efficiency of the supply chain network.

[0134] In order to better implement the supply chain network optimization method in the embodiment of the present application, based on the supply chain network optimization method, the embodiment of the present application also provides a supply chain network optimization device, such as Figure 7 As shown, the supply chain network optimization device 700 includes:

[0135] A data acquisition module 710 is configured to acquire supply chain network configuration data, wherein the supply chain network configuration data includes candidate warehouse data, the number of warehouse levels, and optimization target information;

[0136] A network construction module 720 is configured to construct an initial supply chain network based on the candidate warehouse data and the number of warehouse levels, wherein the network nodes of the initial supply chain network are determined based on the candidate warehouses in the candidate warehouse data;

[0137] A network optimization module 730 is configured to optimize the node states and node relationships of the initial supply chain network based on the optimization target information to obtain an optimized supply chain network;

[0138] The target acquisition module 740 is configured to use the optimized supply chain network as a target supply chain network when the optimized supply chain network meets preset supply chain network indicators.

[0139] In some embodiments of the present application, the network construction module 720 is also used to analyze the number of network warehouses, the candidate warehouse data and the number of warehouse levels based on a preset first adaptation algorithm to construct an initial supply chain network if the supply chain network configuration data also includes the number of network warehouses; if the supply chain network configuration data does not include the number of network warehouses, then analyze the candidate warehouse data and the number of warehouse levels based on a preset first adaptation algorithm to construct an initial supply chain network.

[0140] In some embodiments of the present application, the network optimization module 730 is also used to optimize the node status of each target warehouse node in the initial supply chain network based on a preset disturbance strategy to obtain a preliminarily optimized supply chain network; based on the optimization target information, optimize the node relationship of the preliminarily optimized supply chain network to obtain an optimized supply chain network.

[0141] In some embodiments of the present application, the perturbation strategy is more than one perturbation strategy, the candidate warehouse data includes the number of candidate warehouses, and the network optimization module 730 is also used to determine the first usage ratio of each perturbation strategy based on the number of candidate warehouses and the number of network warehouses if the supply chain network configuration data also includes the number of network warehouses, and based on the first usage ratio, perform node state optimization on each target warehouse node in the initial supply chain network to obtain a preliminarily optimized supply chain network; if the supply chain network configuration data does not include the number of network warehouses, determine the second usage ratio of each perturbation strategy based on the number of candidate warehouses, and based on the second usage ratio, perform node state optimization on each target warehouse node in the initial supply chain network to obtain a preliminarily optimized supply chain network.

[0142] In some embodiments of the present application, the network optimization module 730 is also used to determine target rule information based on the preset coverage rules corresponding to the optimization target information; based on the target rule information, the node relationship of the supply chain network after the preliminary optimization is optimized to obtain an optimized supply chain network; wherein the target rule information is any one of the first rule information, the second rule information and the third rule information.

[0143] In some embodiments of the present application, the supply chain network configuration data also includes constraint information, and the target acquisition module 740 is also used to construct an objective function based on the optimization target information and the constraint information to obtain the supply chain network indicators; based on the objective function, obtain the first function result corresponding to the optimized supply chain network, and obtain the second function result corresponding to the initial supply chain network; when the preferred result between the first function result and the second function result is the first function result, determine that the optimized supply chain network meets the supply chain network indicators, and use the optimized supply chain network as the target supply chain network.

[0144] In some embodiments of the present application, the target acquisition module 740 is also used to, if the objective function is the first objective function or the second objective function, filter out the minimum result of the first function result and the second function result as the preferred result; if the objective function is the third objective function, filter out the maximum result of the first function result and the second function result as the preferred result; when the preferred result is the first function result, determine that the optimized supply chain network meets the supply chain network indicators, and use the optimized supply chain network as the target supply chain network.

[0145] In some embodiments of the present application, the supply chain network optimization device 700 also includes an optimization termination module, which is used to obtain the network optimization duration and / or the number of network optimization iterations when the optimized supply chain network does not meet the objective function, wherein the objective function is constructed based on the optimization target information; if the network optimization duration is greater than or equal to the preset optimization duration threshold, and / or the number of network optimization iterations is greater than or equal to the preset iteration threshold, then the initial supply chain network is used as the target supply chain network; if the network optimization duration is less than the optimization duration threshold, and the number of network optimization iterations is less than the iteration threshold, then the node status optimization and node relationship optimization operations of the optimized supply chain network are repeatedly performed until the repeatedly optimized supply chain network meets the objective function, and then the repeatedly optimized supply chain network is used as the target supply chain network.

[0146] In some embodiments of the present application, the supply chain network optimization device 700 also includes a deep optimization module for optimizing the target supply chain network based on a pre-stored replenishment cycle list to obtain an optimized target supply chain network; based on the order demand data, obtaining the total order demand corresponding to each valid warehouse node, wherein each valid warehouse node is a warehouse node in the optimized target supply chain network; based on the total order demand, the preset replenishment cycle duration and the replenishment cycle list, obtaining the periodic inventory corresponding to each valid warehouse node; based on the periodic inventory and the preset inventory unit price data, obtaining the corresponding total inventory cost of each valid warehouse node; when the total inventory cost meets the preset optimization termination condition, the optimized target supply chain network is used as the preferred supply chain network.

[0147] In the supply chain network optimization device in the above embodiment, the server constructs an initial supply chain network by acquiring supply chain network configuration data including candidate warehouse data, the number of warehouse levels, and optimization target information, and then optimizes the node status and node relationship of the initial supply chain network to achieve the optimization of the initial supply chain network and the acquisition of the optimized network. Finally, it analyzes whether the optimized supply chain network meets the preset supply chain network indicators, so that the optimized supply chain network that meets the supply chain network indicators can be used as the target supply chain network. Since the supply chain network optimization scheme proposed in this application is essentially a warehouse network optimization scheme with "warehouse level enumeration instead of supply relationship enumeration" as the core. At the same time, it is also proposed to adopt a neighborhood search method to obtain an acceptable approximate optimal solution within an appropriate time, avoiding the long computational waiting time for solving the optimal solution. Therefore, the adoption of this scheme can greatly reduce the search space and search time in the network optimization process, thereby effectively improving the optimization efficiency of the supply chain network.

[0148] In some embodiments of the present application, the supply chain network optimization device 700 can be implemented in the form of a computer program. The computer program can be used in Figure 8 The computer device shown in FIG. 1 is run on the computer device shown in FIG. The memory of the computer device can store various program modules constituting the supply chain network optimization device 700, such as: Figure 7 The data acquisition module 710, network construction module 720, network optimization module 730 and target acquisition module 740 are shown. The computer program composed of various program modules enables the processor to execute the steps of the supply chain network optimization method of each embodiment of the present application described in this specification.

[0149] For example, Figure 8 The computer device shown can be Figure 7The data acquisition module 710 in the supply chain network optimization device 700 shown executes step S201. The computer device can execute step S202 through the network construction module 720. The computer device can execute step S203 through the network optimization module 730. The computer device can execute step S204 through the target acquisition module 740. The computer device includes a processor, a memory, and a network interface connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external computer device through a network connection. When the computer program is executed by the processor, a supply chain network optimization method is implemented.

[0150] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0151] In some embodiments of the present application, a computer device is provided, comprising one or more processors; a memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to cause the processor to execute the steps of the above-described supply chain network optimization method. The steps of the supply chain network optimization method herein may be the steps of the supply chain network optimization method in each of the above-described embodiments.

[0152] In some embodiments of the present application, a computer-readable storage medium is provided, storing a computer program. The computer program is loaded by a processor, causing the processor to execute the steps of the above-mentioned supply chain network optimization method. The steps of the supply chain network optimization method here can be the steps of the supply chain network optimization method in each of the above-mentioned embodiments.

[0153] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0154] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0155] The above is a detailed introduction to a supply chain network optimization method, device, computer equipment and storage medium provided in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A supply chain network optimization method, characterized in that: include: Obtaining supply chain network configuration data, wherein the supply chain network configuration data includes candidate warehouse data, the number of warehouse levels, optimization target information, and constraint information; Based on the candidate warehouse data and the number of warehouse levels, construct an initial supply chain network, wherein network nodes of the initial supply chain network are determined according to the candidate warehouses in the candidate warehouse data; Based on a preset perturbation strategy, optimizing the node state of each target warehouse node in the initial supply chain network to obtain a preliminarily optimized supply chain network; Based on the optimization target information, optimizing the node relationships of the initially optimized supply chain network to obtain an optimized supply chain network; Constructing an objective function based on the optimization target information and the constraint condition information to obtain a supply chain network indicator; Based on the objective function, obtaining a first function result corresponding to the optimized supply chain network, and obtaining a second function result corresponding to the initial supply chain network; When the preferred result between the first function result and the second function result is the first function result, it is determined that the optimized supply chain network meets the supply chain network indicators, and the optimized supply chain network is used as the target supply chain network.

2. The method according to claim 1, wherein The constructing of an initial supply chain network based on the candidate warehouse data and the number of warehouse levels includes: If the supply chain network configuration data also includes the number of network warehouses, then based on a preset first adaptation algorithm, the number of network warehouses, the candidate warehouse data, and the number of warehouse levels are analyzed to construct an initial supply chain network; If the supply chain network configuration data does not include the number of network warehouses, the candidate warehouse data and the number of warehouse levels are analyzed based on a preset first adaptation algorithm to construct an initial supply chain network.

3. The method according to claim 1, wherein The perturbation strategy is more than one perturbation strategy, the candidate warehouse data includes the number of candidate warehouses, and the node state optimization is performed on each target warehouse node in the initial supply chain network based on the preset perturbation strategy to obtain a preliminarily optimized supply chain network, including: If the supply chain network configuration data also includes the number of network warehouses, then based on the number of candidate warehouses and the number of network warehouses, a first usage ratio of each perturbation strategy is determined, and based on the first usage ratio, node status optimization is performed on each target warehouse node in the initial supply chain network to obtain a preliminarily optimized supply chain network; If the supply chain network configuration data does not include the number of network warehouses, the second usage ratio of each disturbance strategy is determined based on the number of candidate warehouses, and based on the second usage ratio, the node status of each target warehouse node in the initial supply chain network is optimized to obtain a preliminarily optimized supply chain network.

4. The method according to claim 1, wherein The step of optimizing the node relationships of the initially optimized supply chain network based on the optimization target information to obtain an optimized supply chain network includes: Determining target rule information based on a preset coverage rule corresponding to the optimization target information; Based on the target rule information, optimizing the node relationships of the initially optimized supply chain network to obtain an optimized supply chain network; The target rule information is any one of the first rule information, the second rule information and the third rule information.

5. The method according to claim 4, wherein When the preferred result between the first function result and the second function result is the first function result, determining that the optimized supply chain network meets the supply chain network indicator, and using the optimized supply chain network as the target supply chain network, includes: If the objective function is the first objective function or the second objective function, the minimum value of the first function result and the second function result is selected as the preferred result; If the objective function is the third objective function, the maximum value of the results of the first function and the second function is selected as the preferred result; When the optimization result is the first function result, it is determined that the optimized supply chain network meets the supply chain network index, and the optimized supply chain network is used as the target supply chain network.

6. The method according to any one of claims 1 to 2, wherein: After performing node state optimization and node relationship optimization on the initial supply chain network based on the optimization target information to obtain an optimized supply chain network, the method further includes: When the optimized supply chain network does not satisfy the objective function, obtaining the network optimization duration and / or the number of network optimization iterations, wherein the objective function is constructed according to the optimization target information; If the network optimization time is greater than or equal to a preset optimization time threshold, and / or the number of network optimization iterations is greater than or equal to a preset iteration threshold, the initial supply chain network is used as the target supply chain network; If the network optimization time is less than the optimization time threshold, and the number of network optimization iterations is less than the iteration threshold, the node status optimization and node relationship optimization operations of the optimized supply chain network are repeatedly performed until the repeatedly optimized supply chain network meets the objective function, and the repeatedly optimized supply chain network is used as the target supply chain network.

7. The method according to any one of claims 1 to 2, wherein The supply chain network configuration data also includes order demand data. After the optimized supply chain network is used as the target supply chain network, the following steps are also included: Optimizing the target supply chain network based on a pre-stored replenishment cycle list to obtain an optimized target supply chain network; Based on the order demand data, obtaining the total order demand corresponding to each valid warehouse node, wherein each valid warehouse node is a warehouse node in the optimized target supply chain network; Based on the total order demand, the preset replenishment cycle duration, and the replenishment cycle list, obtaining the cycle inventory corresponding to each valid warehouse node; Based on the periodic inventory quantity and the preset inventory unit price data, the total inventory cost of each valid warehouse node is obtained; When the total inventory cost meets the preset optimization termination condition, the optimized target supply chain network is used as the preferred supply chain network.

8. A supply chain network optimization device, characterized in that: include: A data acquisition module is used to acquire supply chain network configuration data, wherein the supply chain network configuration data includes candidate warehouse data, the number of warehouse levels, optimization target information, and constraint information; a network construction module, configured to construct an initial supply chain network based on the candidate warehouse data and the number of warehouse levels, wherein the network nodes of the initial supply chain network are determined according to the candidate warehouses in the candidate warehouse data; A network optimization module is used to optimize the node status of each target warehouse node in the initial supply chain network based on a preset perturbation strategy to obtain a preliminarily optimized supply chain network; Based on the optimization target information, optimizing the node relationships of the initially optimized supply chain network to obtain an optimized supply chain network; A target acquisition module is used to construct an objective function based on the optimization target information and the constraint condition information to obtain supply chain network indicators; Based on the objective function, obtaining a first function result corresponding to the optimized supply chain network, and obtaining a second function result corresponding to the initial supply chain network; When the preferred result between the first function result and the second function result is the first function result, it is determined that the optimized supply chain network meets the supply chain network indicators, and the optimized supply chain network is used as the target supply chain network.

9. A computer device, characterized in that: The computer device comprises: one or more processors; A memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the supply chain network optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in the supply chain network optimization method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The computer program product comprises a program or instructions, which, when executed, causes a computer to execute the method according to any one of claims 1 to 7.

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