Store replenishment methods, devices, computer equipment and storage media

Abstract

The embodiments of the present application belong to the field of information processing technology, and relate to a method for replenishing items in a store, including obtaining basic replenishment data of historical items, constructing an objective function based on the basic replenishment data, determining constraints based on the basic replenishment data, constructing an initial replenishment model based on the objective function and the constraints, optimizing the initial replenishment model according to a linearization algorithm, obtaining a target replenishment model, obtaining target replenishment data of a target item, inputting the target replenishment data into the target replenishment model, and outputting a decision on the disassembly of the target item. The present application also provides a store item replenishment device, a computer device, and a storage medium. In addition, the present application also relates to blockchain technology, and the basic replenishment data can be stored in a blockchain. The present application can reduce store inventory, improve cash flow turnover, and at the same time reduce the risk of damage to store items, thereby increasing store profitability.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and in particular to a method, device, computer equipment and storage medium for replenishing store items. Background Art

[0002] Currently, in the replenishment strategy of retail chains, most stores are replenishing on the basis of full case delivery. For slow-moving products, full case delivery will lead to a large replenishment volume, which will lead to store inventory backlogs. Excessive backlogs will occupy cash flow. At the same time, stores need to store extra inventory in storage rooms or high-rise shelves, which increases the risk of damage to items. Slow-moving products are often expensive, and damage to items seriously reduces store profitability. Therefore, how to choose a suitable replenishment strategy to reduce store inventory costs and store item damage risks has become an urgent problem to be solved. Summary of the invention

[0003] The purpose of the embodiments of the present application is to propose a method, device, computer equipment and storage medium for replenishing store items to solve the technical problems in the related art that the backlog of inventory in stores causes excessively high inventory costs, occupies the store's cash flow, and at the same time increases the risk of item damage and reduces the store's profitability.

[0004] In order to solve the above technical problems, the embodiment of the present application provides a method for replenishing items in a store, which adopts the following technical solution:

[0005] Get the replenishment basic data of historical items;

[0006] Constructing an objective function based on the replenishment basic data;

[0007] Determining constraint conditions according to the replenishment basic data;

[0008] Building an initial replenishment model based on the objective function and the constraint conditions;

[0009] Optimizing the initial replenishment model according to a linearization algorithm to obtain a target replenishment model;

[0010] Obtain target replenishment data for target items;

[0011] The target replenishment data is input into the target replenishment model, and the split decision of the target item is output.

[0012] Furthermore, the replenishment basic data includes operation data and item data, the operation data includes cash yield rate, single-time splitting cost, business days, number of stores and number of business formats, and the item data includes item price, item box size, average daily sales volume of items and number of item types. The step of constructing the objective function according to the replenishment basic data includes:

[0013] Calculate the average inventory cost of full case replenishment, the average inventory cost of piecemeal replenishment, and the piecemeal operation cost based on the replenishment basic data;

[0014] The objective function is constructed according to the average inventory cost of full case replenishment, the average inventory cost of split-piece replenishment, and the split-piece operation cost, wherein the objective function expression is as follows:

[0015]

[0016] Among them, profit represents the profit from retail sales; α represents the cash rate of return; β represents the cost of retail sales per transaction; y represents the number of business days per year of the store; g represents the number of product categories; r represents the number of stores; f represents the number of business formats; c represents the number of retail stores; i Indicates the price of the item; p i Indicates the size of the item box; d ijt A represents the average daily sales volume of the i-th item in the j-th store under the t-th business format; it Represents the decision to split the i-th item into smaller pieces under the t-th business format.

[0017] Furthermore, the constraint conditions include a constraint condition on the number of times a distribution center splits goods in a single day, a constraint condition on a first split decision, a constraint condition on a box size, and a constraint condition on a second split decision. The step of determining the constraint conditions based on the replenishment basic data includes:

[0018] The constraint condition of the number of times the distribution center splits goods per day is determined according to the following formula:

[0019]

[0020]

[0021] Among them, y represents the number of business days of the store in a year; w represents the number of working days of the distribution center in a year; g represents the number of types of goods; r represents the number of stores; f represents the number of business formats; p i Indicates the size of the item box; d ijt A represents the average daily sales volume of the i-th item in the j-th store under the t-th business format; it represents the split decision of the ith item in the tth business format; X it It means if X it is 0, then A it Equal to p i , if X it is 1, then A it Greater than or equal to 0; m represents the maximum number of times the distribution center splits each day;

[0022] When the split decision is greater than half of the item box size, the first split decision constraint condition is determined according to the following formula:

[0023]

[0024]

[0025] The box gauge constraint condition is determined according to the following formula:

[0026]

[0027]

[0028] When the split decision is between 1 and the full box size, the second split decision constraint is determined according to the following formula:

[0029]

[0030]

[0031] Where Z is a set of integers.

[0032] Furthermore, the step of constructing an initial replenishment model based on the objective function and the constraint conditions includes:

[0033] Based on the objective function and the constraints, the initial replenishment model is constructed using the following formula:

[0034]

[0035] Among them, X it It means if X it is 0, then A it Equal to p i , if X it is 1, then A it Greater than or equal to 0.

[0036] Furthermore, the step of optimizing the initial replenishment model according to the linearization algorithm to obtain the target replenishment model includes:

[0037] Get the first split zero continuous variable and the second split zero continuous variable;

[0038] Performing a linear transformation on the objective function according to the first zero-split continuous variable and the second zero-split continuous variable;

[0039] Performing a linear transformation on the constraint condition according to the first split-zero continuous variable and the second split-zero continuous variable;

[0040] The target replenishment model is obtained based on the linearized objective function and the linearized constraint conditions.

[0041] Furthermore, the step of performing linear transformation on the constraint condition according to the first zero-split continuous variable and the second zero-split continuous variable comprises:

[0042] The constraint on the number of times the distribution center splits items per day is linearized according to the following formula:

[0043]

[0044] Among them, Z it represents the second split zero continuous variable, Z it =λ it X it , λ it represents the first split zero continuous variable;

[0045] The first split decision constraint is linearized according to the following formula:

[0046]

[0047] Among them, Y it represents the introduced zero-one variable,

[0048] Z it =λ it X it Perform the following linear transformation:

[0049]

[0050]

[0051] for Introducing zero-one variables and continuous variables Perform a linear transformation:

[0052]

[0053]

[0054]

[0055]

[0056] Furthermore, the step of obtaining the target replenishment model based on the linearized objective function and the linearized constraint conditions includes:

[0057] Based on the linearized objective function and the linearized constraint conditions, the target replenishment model is obtained by the following formula:

[0058]

[0059] Among them, A ijt represents the split decision of the i-th item in the j-th store under the t-th format; Z ijt represents the second split zero continuous variable, Z ijt =λ ijt X ijt , λ ijt represents the first zero-stripped continuous variable.

[0060] In order to solve the above technical problems, the embodiment of the present application further provides a store item replenishment device, which adopts the following technical solution:

[0061] The acquisition module is used to obtain the basic replenishment data of historical items;

[0062] An objective function building module, used to build an objective function according to the replenishment basic data;

[0063] A constraint condition determination module, used for determining constraint conditions according to the replenishment basic data;

[0064] A model building module, used for building an initial replenishment model based on the objective function and the constraint conditions;

[0065] A linearization module, used for optimizing the initial replenishment model according to a linearization algorithm to obtain a target replenishment model;

[0066] The acquisition module is further used to acquire target replenishment data of the target item;

[0067] The model calculation module is used to input the target replenishment data into the target replenishment model and output the split decision of the target item.

[0068] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0069] The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the above-mentioned store item replenishment method when executing the computer-readable instructions.

[0070] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0071] The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the store item replenishment method described above are implemented.

[0072] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0073] The present application obtains basic replenishment data of historical items, constructs an objective function based on the basic replenishment data, determines constraints based on the basic replenishment data, constructs an initial replenishment model based on the objective function and the constraints, optimizes the initial replenishment model according to a linearization algorithm, obtains a target replenishment model, obtains target replenishment data of target items, inputs the target replenishment data into the target replenishment model, and outputs a split decision for the target items; the target replenishment model constructed by the present application can calculate the optimal split strategy for each item while ensuring the optimal store revenue, thereby reducing store inventory, improving cash flow turnover, and reducing the risk of store item damage, thereby improving store profitability. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0075] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0076] Figure 2 A flow chart of an embodiment of a method for replenishing store items according to the present application;

[0077] Figure 3 is a structural schematic diagram of an embodiment of a store item replenishment device according to the present application;

[0078] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0079] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0080] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0081] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0082] This application provides a store item replenishment method, which can be applied to Figure 1 In the system architecture 100 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0083] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0084] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0085] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0086] It should be noted that the store item replenishment method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the store item replenishment device is generally set in the server / terminal device.

[0087] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0088] Continue to refer Figure 2 , shows a flow chart of an embodiment of a store item replenishment method according to the present application, comprising the following steps:

[0089] Step S201, obtaining the basic replenishment data of historical items.

[0090] Specifically, the replenishment basic data of each store may be obtained from a database, or directly received from a user, or each sensor or smart terminal or device may collect the replenishment basic data of each store in real time.

[0091] In this embodiment, the basic replenishment data includes operation data and item data. The operation data includes but is not limited to cash yield rate, single splitting cost, number of working days of the distribution center in a year, maximum number of splitting times of the distribution center per day, number of business days, number of stores and number of business formats. The item data includes but is not limited to item price, item box size, item safety stock, average daily sales volume of items and number of item types.

[0092] When obtaining the basic replenishment data of each store from the database, a call request needs to be sent to the database, and the call request carries the signature verification token; the signature verification result returned by the database is received, and when the signature verification result is passed, the basic replenishment data of each store in the database is called.

[0093] Data security is ensured by encrypting the database and requiring signature verification when extracting basic replenishment data for each store in the database.

[0094] It should be emphasized that in order to further ensure the privacy and security of the replenishment basic data, the above replenishment basic data can also be stored in a blockchain node.

[0095] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0096] Step S202, constructing an objective function according to the replenishment basic data.

[0097] Retail chain stores can use the method of unpacking to replenish goods, and deliver them to each store through unpacking. The strategy of unpacking replenishment requires unpacking operations at the distribution center, which will bring unpacking costs. Therefore, it is necessary to calculate whether the benefits of unpacking are higher than the operating costs of unpacking. If the benefits of unpacking are higher than the operating costs of unpacking, unpacking can be used for replenishment. If the benefits of unpacking are less than the operating costs of unpacking, unpacking will not be performed and replenishment will still be carried out in the form of full boxes. Therefore, whether the selected unpacking strategy is to distribute in full boxes or in pieces, it is necessary to calculate the unpacking revenue, which can be expressed as follows:

[0098] Revenue from splitting = Average inventory cost of full-case replenishment - (Average inventory cost of splitting + splitting operation cost)

[0099] In this embodiment, the average inventory cost of full-box replenishment, the average inventory cost of split-unit replenishment, and the split-unit operation cost are calculated based on the replenishment basic data. Specifically, the average inventory cost of full-box replenishment is calculated using the following formula:

[0100]

[0101] Among them, α represents the cash yield; c i represents the price of item i; p i Indicates the box size of item i; d ijt represents the average daily sales volume of the i-th item in the j-th store under the t-th business format; u ijt If the average daily sales volume of the i-th item in the j-th store under the t-th format is greater than zero, then u ijt is 1, otherwise, u ijt is 0; s ijt It represents the safety stock of the i-th item in the j-th store under the t-th business format.

[0102] The average inventory cost of split-stock replenishment is calculated using the following formula:

[0103]

[0104] Among them, A it Represents the decision to split the i-th item into smaller pieces under the t-th business format.

[0105] The cost of the disassembly operation is calculated using the following formula:

[0106]

[0107] Among them, β represents the single-time split-up cost; y represents the number of operating days of the store in a year.

[0108] The objective function is constructed based on the average inventory cost of full-case replenishment, the average inventory cost of split-piece replenishment, and the split-piece operation cost. The objective function represents the split-piece profit. The specific expression is as follows:

[0109]

[0110]

[0111] Among them, profit represents the profit from splitting; α represents the cash yield; β represents the single splitting cost; y represents the number of business days of the store in a year; c i Indicates the price of the item; p i Indicates the size of the item box; d ijt represents the average daily sales volume of the i-th item in the j-th store under the t-th business format; u ijt If the average daily sales volume of the i-th item in the j-th store under the t-th format is greater than zero, then u ijt is 1, otherwise, u ijt is 0; A it Represents the decision to split the i-th item into smaller pieces under the t-th business format.

[0112] Need to explain, split decision A it It refers to the number of scattered pieces after the i-th item in the t-th business format is broken down into smaller pieces. For example, the original full box size is 20 pieces, and the current number of scattered pieces after breaking down is 10 pieces, then A it is 10. Business formats refer to the brands of retail chain stores, including but not limited to China Resources Vanguard, Vanguard Go, Ole, Suning, etc. Different retail chain store brands may have different split decisions for the same item.

[0113] Step S203, determining constraint conditions according to the replenishment basic data.

[0114] In this embodiment, the constraints include the number of times the distribution center splits goods per day, the first split decision constraint, the box size constraint, and the second split decision constraint. Specifically, the number of times the distribution center splits goods per day is that the number of box unpacking operations per day cannot exceed the unpacking capacity of the distribution center, and the expression is as follows:

[0115]

[0116] Among them, y represents the number of business days of the store in a year; w represents the number of working days of the distribution center in a year; g represents the number of types of goods; r represents the number of stores; f represents the number of business formats; p i Indicates the size of the item box; d ijt A represents the average daily sales volume of the i-th item in the j-th store under the t-th business format; it represents the split decision of the ith item in the tth business format; X it It means if Xit is 0, then A it Equal to p i , if X it is 1, then A it Greater than or equal to 0; m represents the maximum number of times the distribution center splits items per day.

[0117] The first split decision constraint condition is that when the split decision is greater than half of the item box size, then choose not to split the box. The expression is as follows:

[0118]

[0119] The specific box specification constraints are: if X it is 0, then A it Equal to p i , if X it is 1, then A it Greater than or equal to 0, the expression is as follows:

[0120]

[0121] The second split decision constraint is the constraint when the split decision is between 1 and the full box size, and the expression is as follows:

[0122]

[0123] In addition, the split decision A it If is an integer, then there are the following variable constraints:

[0124]

[0125] X it If is a zero-one variable, then there are the following variable constraints:

[0126]

[0127] Step S204: construct an initial replenishment model based on the objective function and constraints.

[0128] In this embodiment, an initial replenishment model is constructed based on the objective function and constraints as follows. Through this model, the optimal split-up decision can be solved on the basis of maximizing the split-up revenue:

[0129]

[0130] Constraints:

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137] Step S205, optimizing the initial replenishment model according to the linearization algorithm to obtain the target replenishment model.

[0138] In this embodiment, the initial replenishment model is a non-convex nonlinear optimization problem. The initial replenishment model is linearly transformed by a linearization algorithm to improve the computational efficiency.

[0139] Specifically, the first and second continuous variables are obtained, the objective function is linearly transformed according to the first and second continuous variables, the constraints are linearly transformed according to the first and second continuous variables, and the target replenishment model is obtained based on the linearized objective function and the linearized constraints.

[0140] The objective function and constraints can be obtained by introducing the first zero-splitting continuous variable λ it And the second split zero continuous variable Z it Linearization is performed, where Z it =λ it X it .

[0141] In this embodiment, the linearization of the constraints is as follows:

[0142] By Z it The constraint on the number of times a distribution center splits items per day is transformed into the following constraint:

[0143]

[0144] By introducing the zero-one variable Y it Convert the first split decision constraint into the following constraints:

[0145]

[0146] The second continuous variable Z introduced it =λ it X it The linear transformations are as follows:

[0147]

[0148]

[0149] In addition, through the introduction of And zero-one variables and continuous variables It can be transformed into the following constraints:

[0150]

[0151]

[0152]

[0153]

[0154] Based on the linearized objective function and linearized constraints, the target replenishment model is obtained through the following formula:

[0155]

[0156] The constraints corresponding to the target replenishment model are as follows:

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173] The target replenishment model has transformed the problem into a mixed integer programming problem, which can be solved by a solver. Currently, commonly used solvers include Gurobi, Cplex, ortools, SCIP, Lingo, etc.

[0174] Step S206, obtaining target replenishment data of the target item.

[0175] In this embodiment, the target item is a commodity that the store needs to replenish, and the corresponding target replenishment data is obtained. The known target replenishment data include: cash yield rate α, single split cost β, the number of working days of the distribution center in a year w, the maximum number of splits per day of the distribution center m, the number of business days of the store in a year y, the number of stores r and the number of business formats f, the price of the target item c i 、Target item box specifications i , target item safety stock S ijt 、Average daily sales of target items d ijt and the number of target item types g.

[0176] It should be noted that the target replenishment data of the acquired target items is stored in the database for subsequent calling.

[0177] Step S207, input the target replenishment data into the target replenishment model, and output the split decision of the target item.

[0178] In this embodiment, the known target replenishment data is input into the target replenishment model, and the model is solved by a mixed integer programming solver to output the split decision. The output split decision is the best feasible solution, which maximizes the split benefit of the objective function.

[0179] By constructing a target replenishment model, this application can calculate the optimal split-up strategy for each item while ensuring the optimal store revenue, thereby reducing store inventory, improving cash flow turnover, and reducing the risk of damage to store items, thereby increasing store profitability.

[0180] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0181] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0182] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0183] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a store item replenishment device, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0184] like Figure 3As shown, the store item replenishment device 300 described in this embodiment includes: an acquisition module 301, an objective function construction module 302, a constraint condition determination module 303, a model construction module 304, a linearization module 305 and a model calculation module 306. Among them:

[0185] The acquisition module 301 is used to acquire the replenishment basic data of historical items;

[0186] The objective function construction module 302 is used to construct an objective function according to the replenishment basic data;

[0187] The constraint condition determination module 303 is used to determine the constraint condition according to the replenishment basic data;

[0188] The model building module 304 is used to build an initial replenishment model based on the objective function and the constraint conditions;

[0189] The linearization module 305 is used to optimize the initial replenishment model according to the linearization algorithm to obtain a target replenishment model;

[0190] The acquisition module 301 is also used to acquire target replenishment data of the target item;

[0191] The model calculation module 306 is used to input the target replenishment data into the target replenishment model and output the split decision of the target item.

[0192] It should be emphasized that in order to further ensure the privacy and security of the replenishment basic data, the above replenishment basic data can also be stored in a blockchain node.

[0193] The above-mentioned store item replenishment device, through the constructed target replenishment model, can calculate the optimal disassembly strategy for each item under the condition of ensuring the optimal store revenue, thereby reducing store inventory, improving cash flow turnover, and reducing the risk of damage to store items, thereby improving store profitability.

[0194] In this embodiment, the objective function construction module 302 is further used to:

[0195] Calculate the average inventory cost of full case replenishment, the average inventory cost of piecemeal replenishment, and the piecemeal operation cost based on the replenishment basic data;

[0196] The objective function is constructed according to the average inventory cost of full case replenishment, the average inventory cost of split-piece replenishment, and the split-piece operation cost, wherein the objective function expression is as follows:

[0197]

[0198] Among them, profit represents the profit from splitting; α represents the cash yield; β represents the single splitting cost; y represents the number of business days of the store in a year; c i Indicates the price of the item; p i Indicates the size of the item box; d ijt A represents the average daily sales volume of the i-th item in the j-th store under the t-th business format; it Represents the decision to split the i-th item into smaller pieces under the t-th business format.

[0199] In this embodiment, the constraints include the number of times the distribution center splits in a single day, the first split decision constraint, the box size constraint, and the second split decision constraint. The constraint determination module 303 is further used to:

[0200] The constraint condition of the number of times the distribution center splits goods per day is determined according to the following formula:

[0201]

[0202]

[0203] Among them, y represents the number of business days of the store in a year; w represents the number of working days of the distribution center in a year; g represents the number of types of goods; r represents the number of stores; f represents the number of business formats; p i Indicates the size of the item box; d ijt A represents the average daily sales volume of the i-th item in the j-th store under the t-th business format; it represents the split decision of the ith item in the tth business format; X it It means if X it is 0, then A it Equal to p i , if X it is 1, then A it Greater than or equal to 0; m represents the maximum number of times the distribution center splits each day;

[0204] When the split decision is greater than half of the item box size, the first split decision constraint condition is determined according to the following formula:

[0205]

[0206]

[0207] The box gauge constraint condition is determined according to the following formula:

[0208]

[0209]

[0210] When the split decision is between 1 and the full box size, the second split decision constraint is determined according to the following formula:

[0211]

[0212]

[0213] Where Z is a set of integers.

[0214] In this embodiment, the model building module 304 is further used to:

[0215] Based on the objective function and the constraints, the initial replenishment model is constructed using the following formula:

[0216]

[0217] Among them, X it It means if X it is 0, then A it Equal to p i , if X it is 1, then A it Greater than or equal to 0.

[0218] In this embodiment, the linearization module 305 includes an acquisition submodule, a conversion submodule and a construction submodule, wherein:

[0219] The acquisition submodule is used to acquire the first split-zero continuous variable and the second split-zero continuous variable;

[0220] The transformation submodule is used to perform linear transformation on the objective function according to the first split-zero continuous variable and the second split-zero continuous variable;

[0221] The conversion submodule is further used to perform linear conversion on the constraint condition according to the first split-zero continuous variable and the second split-zero continuous variable;

[0222] The construction submodule is used to obtain the target replenishment model based on the linearized objective function and the linearized constraint conditions.

[0223] In this embodiment, the conversion submodule is further used for:

[0224] The constraint on the number of times the distribution center splits items per day is linearized according to the following formula:

[0225]

[0226] Among them, Z it represents the second split zero continuous variable, Z it =λ it X it , λ it represents the first split zero continuous variable;

[0227] The first split decision constraint is linearized according to the following formula:

[0228]

[0229] Among them, Y it represents the introduced zero-one variable,

[0230] Z it =λ it X it Perform the following linear transformation:

[0231]

[0232]

[0233] for Introducing zero-one variables and continuous variables Perform a linear transformation:

[0234]

[0235]

[0236]

[0237]

[0238] In this embodiment, the construction submodule is further used to:

[0239] Based on the linearized objective function and the linearized constraint conditions, the target replenishment model is obtained by the following formula:

[0240]

[0241] Among them, A ijt represents the split decision of the i-th item in the j-th store under the t-th format; Z ijt represents the second split zero continuous variable, Z ijt =λ ijt X ijt , λ ijt represents the first zero-stripped continuous variable.

[0242] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4This is a basic structural block diagram of the computer device in this embodiment.

[0243] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0244] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0245] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the store item replenishment method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0246] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for running the store item replenishment method.

[0247] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0248] This embodiment implements the steps of the store item replenishment method in the above embodiment when the processor executes computer-readable instructions stored in the memory. The constructed target replenishment model can calculate the optimal splitting strategy for each item under the condition of ensuring the optimal store revenue, thereby reducing store inventory, improving cash flow turnover, and reducing the risk of store item damage, thereby improving store profitability.

[0249] The present application also provides another implementation mode, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor, so that the at least one processor executes the steps of the above-mentioned store item replenishment method, and the constructed target replenishment model can calculate the optimal splitting strategy for each item under the condition of ensuring the optimal store revenue, thereby reducing store inventory, improving cash flow turnover, and reducing the risk of damage to store items, thereby improving store profitability.

[0250] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0251] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for replenishing store items, characterized in that: The steps include: Obtaining historical replenishment basic data of items; the replenishment basic data includes operation data and item data, the operation data includes cash yield rate, single-item splitting cost, business days, number of stores and number of business formats, and the item data includes item price, item box size, average daily sales volume of items and number of item types; Constructing an objective function based on the replenishment basic data; Determine constraints based on the replenishment basic data, wherein the constraints include a constraint on the number of times a distribution center splits goods per day, a constraint on the first split decision, a constraint on the box size, and a constraint on the second split decision; Building an initial replenishment model based on the objective function and the constraint conditions; Optimizing the initial replenishment model according to a linearization algorithm to obtain a target replenishment model; Obtain target replenishment data for target items; Inputting the target replenishment data into the target replenishment model, and outputting the split decision of the target item; The step of constructing an objective function according to the replenishment basic data includes: Calculate the average inventory cost of full case replenishment, the average inventory cost of piecemeal replenishment, and the piecemeal operation cost based on the replenishment basic data; The objective function is constructed according to the average inventory cost of full case replenishment, the average inventory cost of split-piece replenishment, and the split-piece operation cost, wherein the objective function expression is as follows: ； Among them, profit represents the profit from splitting; α represents the cash yield; β represents the cost of splitting a single time; y represents the number of business days of the store in a year; Indicates the price of the item; p i Indicates the size of the item box; d ijt A represents the average daily sales volume of the i-th item in the j-th store under the t-th business format; it represents the split decision of the ith item under the tth business model; The step of constructing an initial replenishment model based on the objective function and the constraint conditions comprises: Based on the objective function and the constraints, the initial replenishment model is constructed using the following formula: ； Among them, g represents the number of product types; r represents the number of stores; f represents the number of business formats; X it It means if X it is 0, then A it Equal to p i , if X it is 1, then A it Greater than 0; u ijt represents the average daily sales volume of the i-th item in the j-th store under the t-th format. If it is greater than zero, then u ijt is 1, otherwise, u ijt is 0; The step of optimizing the initial replenishment model according to the linearization algorithm to obtain the target replenishment model comprises: Get the first split zero continuous variable and the second split zero continuous variable; Performing a linear transformation on the objective function according to the first zero-split continuous variable and the second zero-split continuous variable; Performing a linear transformation on the constraint condition according to the first split-zero continuous variable and the second split-zero continuous variable; Obtaining the target replenishment model based on the linearized objective function and the linearized constraint conditions; The step of obtaining the target replenishment model based on the linearized objective function and the linearized constraint conditions comprises: Based on the linearized objective function and the linearized constraint conditions, the target replenishment model is obtained by the following formula: ; in, represents the split decision of the i-th item in the j-th store under the t-th business format; represents the second split zero continuous variable, , , represents the first split zero continuous variable; u ijt represents the average daily sales volume of the i-th item in the j-th store under the t-th format. If it is greater than zero, then u ijt is 1, otherwise, u ijt is 0.

2. The store item replenishment method according to claim 1, characterized in that: The step of determining the constraint conditions according to the replenishment basic data comprises: The constraint condition of the number of times the distribution center splits goods per day is determined according to the following formula: ; ； Among them, y represents the number of business days of the store in a year; w represents the number of working days of the distribution center in a year; g represents the number of types of goods; r represents the number of stores; f represents the number of business formats; p i Indicates the size of the item box; d ijt A represents the average daily sales volume of the i-th item in the j-th store under the t-th business format; it represents the split decision of the ith item in the tth business format; X it It means if X it is 0, then A it Equal to p i , if X it is 1, then A it Greater than 0; m represents the maximum number of times the distribution center splits items every day; When the split decision is greater than half of the item box size, the first split decision constraint condition is determined according to the following formula: ； ； The box gauge constraint condition is determined according to the following formula: ; ； When the split decision is between 1 and the full box size, the second split decision constraint is determined according to the following formula: ； ； Where Z is a set of integers.

3. The method for replenishing store items according to claim 1, characterized in that: The step of linearly transforming the constraint condition according to the first zero-split continuous variable and the second zero-split continuous variable comprises: The constraint on the number of times the distribution center splits items per day is linearized according to the following formula: ； Among them, Z it represents the second split zero continuous variable, , , represents the first split zero continuous variable; The first split decision constraint is linearized according to the following formula: ； in, represents the introduced zero-one variable, ; Will Perform the following linear transformation: ； ； for , introduce zero-one variables and continuous variables Perform a linear transformation: ； ； ； 。 4. A store item replenishment device, characterized in that: include: An acquisition module is used to acquire basic replenishment data of historical items; the basic replenishment data includes operation data and item data, the operation data includes cash yield rate, single-item splitting cost, business days, number of stores and number of business formats, and the item data includes item price, item box size, average daily sales volume of items and number of item types; An objective function building module, used to build an objective function according to the replenishment basic data; A constraint determination module, used to determine the constraint conditions according to the replenishment basic data, wherein the constraint conditions include the number of split-up times per day of the distribution center, the first split-up decision constraint condition, the box size constraint condition, and the second split-up decision constraint condition; A model building module, used for building an initial replenishment model based on the objective function and the constraint conditions; A linearization module, used for optimizing the initial replenishment model according to a linearization algorithm to obtain a target replenishment model; The acquisition module is further used to acquire target replenishment data of the target item; A model calculation module, used for inputting the target replenishment data into the target replenishment model and outputting the split decision of the target item; The objective function building module is further used for: Calculate the average inventory cost of full case replenishment, the average inventory cost of piecemeal replenishment, and the piecemeal operation cost based on the replenishment basic data; The objective function is constructed according to the average inventory cost of full case replenishment, the average inventory cost of split-piece replenishment, and the split-piece operation cost, wherein the objective function expression is as follows: ； Among them, profit represents the profit from splitting; α represents the cash yield; β represents the cost of splitting a single time; y represents the number of business days of the store in a year; Indicates the price of the item; p i Indicates the size of the item box; d ijt A represents the average daily sales volume of the i-th item in the j-th store under the t-th business format; it represents the split decision of the ith item under the tth business model; The model building module is further used to: Based on the objective function and the constraints, the initial replenishment model is constructed using the following formula: ； Among them, g represents the number of product types; r represents the number of stores; f represents the number of business formats; X it It means if X it is 0, then A it Equal to p i , if X it is 1, then A it greater than 0; u ijt represents the average daily sales volume of the i-th item in the j-th store under the t-th format. If it is greater than zero, then u ijt is 1, otherwise, u ijt is 0; The linearization module includes an acquisition submodule, a conversion submodule and a construction submodule, wherein: The acquisition submodule is used to acquire the first split-zero continuous variable and the second split-zero continuous variable; The transformation submodule is used for performing a linear transformation on the objective function according to the first split-zero continuous variable and the second split-zero continuous variable; The conversion submodule is further used to perform linear conversion on the constraint condition according to the first split-zero continuous variable and the second split-zero continuous variable; The construction submodule is used to obtain the target replenishment model based on the linearized objective function and the linearized constraint conditions; The construction submodule is further used to: Based on the linearized objective function and the linearized constraint conditions, the target replenishment model is obtained by the following formula: ; in, represents the split decision of the i-th item in the j-th store under the t-th business format; represents the second split zero continuous variable, , , represents the first split zero continuous variable; u ijt represents the average daily sales volume of the i-th item in the j-th store under the t-th format. If it is greater than zero, then u ijt is 1, otherwise, u ijt is 0.

5. A computer device, comprising a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the store item replenishment method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the store item replenishment method according to any one of claims 1 to 3 are implemented.

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