Method, device, electronic device and program product for adjusting the allocation of goods inventory

Abstract

The present application provides a method, device, electronic device, and program product for adjusting the allocation of goods inventory, relating to the field of intelligent supply chain technology. A method for adjusting the allocation of goods inventory, comprising: determining recommended goods allocation parameters; calibrating the recommended goods allocation parameters according to preset goods clearance parameters to determine calibrated goods allocation parameters; and generating goods inventory allocation parameters according to the calibrated goods allocation parameters. According to the embodiments of the present application, it is possible to effectively deal with uncertainties in the goods allocation process, generate implementable goods allocation parameters, and eliminate the impact of uncertainties on the goods allocation parameters.

Description

Technical Field

[0001] The present application relates to the field of intelligent supply chain technology, and more specifically, to a method, device, electronic device, and program product for allocating and adjusting inventory of goods. Background Art

[0002] To provide consumers with a better shopping experience, service and timeliness have become increasingly important supply chain metrics. Suppliers cannot directly supply all warehouses with goods. They must allocate goods to warehouses closer to consumers during operations. Over-allocation during this allocation process can lead to stockouts in upstream warehouses and unsold goods in downstream warehouses; insufficient allocation can result in low local fulfillment rates and an inability to meet timeliness requirements. Managing the high uncertainty of demand and ensuring accurate allocation presents a significant challenge for suppliers.

[0003] The inventors of this application discovered that traditional stochastic inventory optimization methods, which assume random variables follow a known distribution, are ineffective for the large appliance industry, which has low sales volume and high product value. The key to improving customer service experience is to consider factors such as downstream warehouse capacity constraints, transportation line capacity limitations, and upstream warehouse outbound SKU width restrictions while optimizing operations to produce feasible product allocation parameters. Summary of the Invention

[0004] The present application provides a method, device, electronic device, and program product for adjusting the allocation of goods inventory. Based on a goods allocation optimization model, the method fully considers the uncertainty of factors such as goods sales volume, eliminates the impact of uncertain factors on goods allocation parameters, and generates implementable goods allocation parameters.

[0005] According to one aspect of the present application, a method for adjusting the allocation of goods inventory is provided, comprising: determining recommended goods allocation parameters; calibrating the recommended goods allocation parameters according to preset goods clearance parameters to determine calibrated goods allocation parameters; and generating goods inventory allocation parameters according to the calibrated goods allocation parameters.

[0006] According to some embodiments, determining the recommended allocation parameters for goods includes: obtaining a priority allocation weight coefficient for the goods; and determining the recommended allocation parameters for the goods based on the priority allocation weight coefficient.

[0007] According to some embodiments, the priority allocation weight coefficient of the goods includes: a first weight coefficient, which is the priority allocation weight coefficient of the goods greater than or equal to a preset first threshold; and a second weight coefficient, which is the priority allocation weight coefficient of the goods less than or equal to a preset second threshold.

[0008] According to some embodiments, determining the recommended product allocation parameters based on the priority allocation weight coefficient includes: constructing a first model based on the first weight coefficient and the second weight coefficient; and determining the recommended product allocation parameters based on the first model and constraints of the first model; the first model is expressed by the following formula: Wherein, α is the first weight coefficient, β is the second weight coefficient, and, The constraints of the first model are Where I is the set of goods, J is the set of warehouses, L (l = (j1, j2)) is the set of routes, v i is the volume parameter of the goods, Q i is the number of items in a full box, s ij V is the storage parameter of goods in the warehouse. j is the available volume parameter of the warehouse, The maximum number of pieces of goods transported on the trunk line, SKU j The maximum outbound SKU width parameter of the upstream warehouse. The target inventory level parameter for the goods in the warehouse. is the variable for the number of boxes of goods transferred, To store the goods in the downstream warehouse after the transfer, 0, 1 variables indicating whether goods and routes are allocated, r ij A 0, 1 variable indicating whether the goods and warehouse are transferred.

[0009] According to some embodiments, the recommended allocation parameters of the goods are calibrated according to preset goods clearance parameters to determine the calibrated allocation parameters of the goods, including: determining the local fulfillment rate of the goods according to the goods clearance parameters; determining the calibrated allocation parameters of the goods according to the local fulfillment rate of the goods and the first model.

[0010] According to some embodiments, determining the calibration allocation parameters of the goods based on the local satisfaction rate of the goods and the first model includes: constructing a second model based on the local satisfaction rate of the goods and the first model; determining the calibration allocation parameters of the goods through the second model and the fuzzy set of the second model; the second model is expressed by the following formula Among them, α is the first weight coefficient in the second model, β is the second weight coefficient in the second model, and δ is the weight coefficient of the local satisfaction rate of the goods in the second model; the fuzzy set of the second model is in, is the random variable of the demand for goods in the warehouse, μ ij is the mean parameter of the product’s sales volume in the warehouse, is the variance parameter of the product sales volume in the warehouse, l ij is the minimum parameter for product sales in the warehouse, m ij It is the maximum parameter of the product’s in-warehouse sales volume.

[0011] According to some embodiments, generating the goods inventory transfer parameters based on the goods calibration transfer parameters includes: generating a third model based on the second model; determining a calculation result of the goods calibration transfer parameters through the third model; and generating the goods inventory transfer parameters based on the calculation result.

[0012] According to some embodiments, generating a third model based on the second model includes: performing a dual transformation on the second model; and performing piecewise linear approximation on the second model that has undergone the dual transformation to obtain the third model.

[0013] According to one aspect of the present application, a device for adjusting the allocation of goods inventory is provided, comprising: an input module for acquiring data required for generating recommended goods allocation parameters and calibrated goods allocation parameters, and acquiring goods clearance parameters; a calculation module for constructing a first model to determine the recommended goods allocation parameters, constructing a second model based on the first model and the goods clearance parameters to determine the calibrated goods allocation parameters, and acquiring calculation results of the calibrated goods allocation parameters by converting the second model; and an output module for outputting the calculation results of the calibrated goods allocation parameters as goods inventory allocation parameters.

[0014] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0015] According to one aspect of the present application, a computer program product is provided, comprising a computer program or instructions, wherein the computer program or instructions implement the aforementioned method when executed by a processor.

[0016] According to the embodiments of the present application, the uncertainty of product sales factors is taken into account and processed through the distributed robust optimization method, so as to achieve the precise allocation of products between upstream warehouses and downstream warehouses, and achieve the goal of local satisfaction rate of products with the lowest possible product inventory and turnover, so as to improve the customer service experience.

[0017] It should be understood that the foregoing general description and the following detailed description are merely illustrative and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] Figure 1 A flow chart illustrating a method for adjusting the allocation of goods inventory according to an exemplary embodiment of the present application is shown.

[0020] Figure 2 A flowchart of determining recommended goods transfer parameters according to an exemplary embodiment of the present application is shown.

[0021] Figure 3 A flowchart of determining parameters for calibrating transfer of goods according to an exemplary embodiment of the present application is shown.

[0022] Figure 4 A flowchart of generating product inventory transfer parameters according to an exemplary embodiment of the present application is shown.

[0023] Figure 5 A linear fitting schematic diagram according to an exemplary embodiment of the present application is shown.

[0024] Figure 6 A block diagram of a device for adjusting the allocation of goods inventory according to an exemplary embodiment of the present application is shown.

[0025] Figure 7 A block diagram of an electronic device according to an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. Like reference numerals in the drawings represent like or similar parts, and thus repetitive description thereof will be omitted.

[0027] The described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of these specific details, or other modes, components, materials, devices or operations may be employed. In these cases, well-known structures, methods, devices, implementations, materials or operations will not be shown or described in detail.

[0028] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0029] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0030] The present application provides a method, device, electronic device and program product for adjusting the allocation of goods inventory. Based on a goods allocation optimization model, the method uses a distributed robust optimization method to consider and process the uncertainty of goods sales factors, eliminate the influence of goods sales factors on goods allocation parameters, and realize the precise allocation of goods between upstream warehouses and downstream warehouses, so as to achieve the goal of local satisfaction rate of goods with the lowest possible goods inventory and turnover.

[0031] The following will describe in detail a method, device, electronic device, and program product for adjusting the allocation of goods inventory according to an embodiment of the present application with reference to the accompanying drawings.

[0032] This application refers to the following terms:

[0033] Transfer: In a warehouse network system, the inventory transfer and balancing process of transferring goods from one warehouse to another.

[0034] Local fulfillment rate: The ratio of a warehouse's sales in the first-priority coverage area to the amount of demand satisfied by the warehouse's inventory, divided by the total sales in the first-priority coverage area.

[0035] Fuzzy set: A description of a distribution set of non-deterministic parameters. It can be considered as a constraint in the solution process.

[0036] Figure 1 A flow chart illustrating a method for adjusting the allocation of goods inventory according to an exemplary embodiment of the present application is shown.

[0037] like Figure 1 As shown, in step S110, the allocation adjustment device determines the recommended allocation parameters of the goods.

[0038] For example, in step S110, the allocation adjustment device obtains the priority allocation weight coefficients of the goods, including a first weight coefficient and a second weight coefficient. The allocation adjustment device constructs a first model based on the first weight coefficient and the second weight coefficient, and determines the recommended allocation parameters of the goods based on the first model and the constraints of the first model.

[0039] In step S120 , the allocation adjustment device calibrates the recommended allocation parameters of the goods according to the preset goods clearance parameters to determine the calibrated allocation parameters of the goods.

[0040] For example, in step S120, the allocation adjustment device determines the local fulfillment rate of the goods according to the goods clearance parameters, constructs a second model based on the first model and the local fulfillment rate of the goods, and determines the goods calibration allocation parameters through the second model and the fuzzy set of the second model.

[0041] In step S130 , the allocation adjustment device generates a product inventory allocation parameter according to the product calibration allocation parameter.

[0042] For example, in step S130, the allocation adjustment device performs a dual transformation on the second model and then performs piecewise linear approximation on the transformed second model to obtain a third model. The allocation adjustment device outputs the calculation results of the product calibration allocation parameters based on the third model and generates product inventory allocation parameters based on the calculation results of the product calibration allocation parameters.

[0043] According to an embodiment of the present application, the allocation adjustment device fully considers the uncertainty of factors such as product sales when optimizing the deterministic model of product inventory allocation, eliminates the impact of uncertain factors on product allocation parameters, and generates product allocation parameters that can be implemented.

[0044] Figure 2 A flowchart of determining recommended goods transfer parameters according to an exemplary embodiment of the present application is shown.

[0045] like Figure 2 As shown, the above step S110 includes steps S111, S112 and S113. In step S111, the allocation adjustment device obtains the priority allocation weight coefficient of the goods.

[0046] For example, in step S111, the allocation adjustment device obtains a first weight coefficient α and a second weight coefficient β according to the inventory of the goods in the warehouse and the target inventory of the goods in the warehouse, wherein: s ij Store parameters for goods in the warehouse, It is the target inventory level parameter of the goods in the warehouse.

[0047] The first weight coefficient α is a priority allocation weight coefficient for goods that are greater than or equal to a preset first threshold, and is used as a priority allocation weight coefficient for goods with high demand.

[0048] The second weight coefficient β is a priority allocation weight coefficient for goods that are less than or equal to a preset second threshold value, and is used as a priority allocation weight coefficient for goods that are out of stock.

[0049] In step S112, the allocation adjustment device constructs a first model according to the priority allocation weight coefficient.

[0050] For example, in step S112, the allocation adjustment device constructs a first model based on the first weight coefficient α corresponding to the goods with high demand and the second weight coefficient β corresponding to the goods in out-of-stock state to obtain the recommended allocation parameters of the goods.

[0051] The first model is expressed by the following formula (1):

[0052]

[0053] Among them, α is the first weight coefficient, β is the second weight coefficient, Q is the variable for the number of boxes transferred. i The parameter for the number of items in a full box (i.e. the box size parameter).

[0054] In step S113, the allocation adjustment device determines the recommended allocation parameters of the goods according to the first model and the constraints of the first model.

[0055] For example, in step S113, the allocation adjustment device sets multiple constraint conditions of the first model, which are expressed by the following formulas (2)-(8):

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] Where I is the set of goods, J is the set of warehouses, L(l=(j1,j2)) is the set of routes, v i is the volume parameter of the goods, Q iis the number of items in a full box, s ij V is the storage parameter of goods in the warehouse. j is the available volume parameter of the warehouse, The maximum number of pieces of goods transported on the trunk line, SKU j The maximum outbound SKU width parameter of the upstream warehouse. The target inventory level parameter for the goods in the warehouse. is the variable for the number of boxes of goods transferred, To store the goods in the downstream warehouse after the transfer, is a 0,1 variable indicating whether the goods and routes are allocated, r ij A 0,1 variable indicating whether the goods and warehouse are transferred.

[0064] The constraint corresponding to formula (2) is that the inventory level after allocation is the sum of the existing inventory and the allocation amount, and the inventory level after allocation does not exceed the target inventory level of the goods in the warehouse.

[0065] The constraint corresponding to formula (3) is that the total amount of goods transferred does not exceed the adjustable inventory of the upstream warehouse.

[0066] The constraint corresponding to formula (4) is that the transfer volume between the upstream warehouse and the downstream warehouse does not exceed the maximum transportation capacity (number of pieces) of the trunk line transportation goods.

[0067] The constraint condition corresponding to formula (5) is that the storage capacity (volume) of the transfer destination warehouse does not exceed the available storage capacity (volume) of the destination warehouse.

[0068] The constraints corresponding to formulas (6)-(8) are that the width of the SKU shipped out of the warehouse does not exceed the width allowed by the warehouse, where M is an infinite value and its specific value is determined according to the parameter scale. For example, the value of M in formula (6) can be 10,000.

[0069] The allocation adjustment device calculates and determines the recommended allocation parameters of the goods through formula (1) according to the constraints of the first model corresponding to formula (1) and the first model corresponding to formulas (2)-(8).

[0070] According to an embodiment of the present application, the allocation adjustment device constructs a deterministic operations model (i.e., the first model) to optimize the allocation quantity of goods while taking into account factors such as downstream warehouse capacity constraints, transportation trunk line capacity limitations, upstream warehouse outbound SKU width limitations, and combination product set requirements, and outputs executable goods allocation parameters.

[0071] Figure 3 A flowchart of determining parameters for calibrating transfer of goods according to an exemplary embodiment of the present application is shown.

[0072] like Figure 3As shown, the above step S120 includes steps S121, S122 and S123. In step S121, the allocation adjustment device determines the local satisfaction rate of the goods according to the goods clearance parameters.

[0073] For example, in step S121, the allocation adjustment device determines the local satisfaction rate of goods according to the preset goods clearance parameter and the preset local satisfaction rate weight parameter δ.

[0074] In step S122, the allocation adjustment device constructs a second model based on the local satisfaction rate of the goods and the first model.

[0075] For example, in step S122, the allocation adjustment device constructs a second model based on the local satisfaction rate of the goods and the first model to obtain the goods calibration allocation parameters. The second model is expressed by the following formula (9):

[0076]

[0077] Among them, α is the first weight coefficient in the second model, β is the second weight coefficient in the second model, and δ is the weight coefficient of the local satisfaction rate of goods in the second model.

[0078] In step S123 , the allocation adjustment device determines the goods calibration allocation parameters according to the second model and the fuzzy set of the second model.

[0079] For example, in step S123, the allocation adjustment device obtains the fuzzy set of the second model, which is expressed by the following formula (10):

[0080]

[0081] in, is the random variable of the demand for goods in the warehouse, μ ij is the mean parameter of the product’s sales volume in the warehouse, is the variance parameter of the product sales volume in the warehouse, l ij is the minimum parameter for product sales in the warehouse, m ij It is the maximum parameter of the product’s in-warehouse sales volume.

[0082] The fuzzy set corresponding to formula (10) is the distribution set of product clearance parameters, that is, the sales plan or forecast of product sales. Product clearance parameters are uncertain.

[0083] The allocation adjustment device determines the goods calibration allocation parameters through the fuzzy set of the second model corresponding to formula (9) and the second model corresponding to formula (10).

[0084] According to an embodiment of the present application, the allocation adjustment device adopts a distributed robust optimization model (i.e., the second model), takes into account the uncertainty and volatility of sales of different goods, and appropriately allocates goods, thereby improving the problem of unsatisfactory local satisfaction rate indicators of goods caused by the deterministic operations model and improving the prediction accuracy of the model.

[0085] Figure 4 A flowchart of generating product inventory transfer parameters according to an exemplary embodiment of the present application is shown.

[0086] like Figure 4 As shown, the above step S130 includes steps S131, S132 and S133. In step S131, the allocation adjustment device performs dual transformation on the second model.

[0087] For example, in step S131, the allocation and adjustment device performs two dual transformations on the second model to eliminate non-deterministic parameters, so that the second model is equivalently transformed into a SOCP model (i.e., a second-order cone optimization model) that can be directly solved by the solver. After the dual transformation, the second model is expressed by the following formula (11):

[0088]

[0089] Among them, α is the first weight coefficient in the second model, β is the second weight coefficient in the second model, and λ is the dual variable introduced in the dual transformation process.

[0090] The constraints of the second model after dual transformation include the constraints expressed by the above formulas (2)-(8) and the dual constraints expressed by the following formulas (12)-(19):

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099] Among them, t and ρ are dual variables introduced in the dual transformation process.

[0100] In step S132, the allocation and adjustment device performs piecewise linear approximation on the second model that has undergone dual transformation to obtain a third model.

[0101] For example, in step S132, the allocation adjustment device performs piecewise linear approximation on the quadratic expression of the sales variance in the fuzzy set of the second model, which is expressed by the following formula (20):

[0102]

[0103] Among them, (D-μ) is regarded as a variable, and the piecewise linear approximation of the quadratic expression of the sales variance is x 2 ≤σ 2 For example, Figure 5 As shown, the dial adjustment device can select 5 groups of line segments: (-4, -4), (-2, -1), (0, 0), (2, -1), (4, -4) for linear fitting.

[0104] The allocation adjustment device performs two dual transformations on the second model after piecewise linear approximation to obtain a third model. The third model is a MILP model (mixed integer linear programming model) with higher solution efficiency than the second model. The third model is expressed by the following formula (21):

[0105]

[0106] Among them, α is the first weight coefficient in the second model, β is the second weight coefficient in the second model, and λ is the dual variable introduced in the dual transformation process.

[0107] The constraints of the third model include the constraints expressed by the above formulas (2)-(8) and the dual constraints expressed by the following formulas (22)-(28):

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] Among them, a, b and τ are dual variables introduced in the dual transformation process.

[0116] In step S133 , the allocation adjustment device generates product inventory allocation parameters through the third model.

[0117] For example, in step S133, the allocation adjustment device calculates the third model to obtain the calculation results of the product calibration allocation parameters. The allocation adjustment device generates the final product inventory allocation parameters based on the actual conditions of the products and transportation routes based on the calculation results of the product calibration allocation parameters.

[0118] For example, the inventory transfer parameters calculated and output by the transfer adjustment device through the third model may correspond to multiple recommended transfer quantities of goods, as shown in Table 1:

[0119] Source warehouse code Destination warehouse code Product ID Recommended allocation amount QDHEWL-0080 QDHEWL-0097 646287235411 15 items QDHEWL-0080 QDHEWL-0098 650586256233 3 items

[0120] Table 1

[0121] The transfer adjustment device generates a calculated recommended transfer quantity every morning. Merchants can adjust these recommended quantities. For example, the recommended quantities in Table 1 could be adjusted to 10 and 3 pieces. After confirmation, the merchant issues the task and generates a transfer execution order. Upon receiving the transfer execution order, the supplier begins warehouse operations, transferring goods between upstream and downstream warehouses through logistics.

[0122] According to the embodiment of the present application, the allocation adjustment device takes into account and processes the uncertainty and volatility of parameters related to product sales by optimizing the second model, greatly improving the solution efficiency, ensuring that the final product inventory allocation parameters can be implemented and improving the local satisfaction rate of the products.

[0123] Figure 6 A block diagram of a device for adjusting the allocation of goods inventory according to an exemplary embodiment of the present application is shown.

[0124] like Figure 6 As shown, the allocation adjustment device includes an input module 210 , a calculation module 220 and an output module 230 .

[0125] The input module 210 is used to obtain data required in the calculation process of the first model, the second model and the third model to generate recommended product allocation parameters and calibrated product allocation parameters.

[0126] For example, the data obtained by the input module 210 includes a set of goods, a set of warehouses, a set of routes, a parameter for the volume of goods, a parameter for the number of pieces in a whole box of goods, a parameter for the inventory of goods in the warehouse, a parameter for the available volume of the warehouse, a parameter for the maximum number of pieces of goods transported on the trunk line, a parameter for the maximum outbound SKU width of the upstream warehouse, a parameter for the target inventory level of goods in the warehouse, a variable for the number of boxes of goods transferred, a variable for the inventory of goods in the downstream warehouse after transfer, a 0,1 variable for whether goods and routes are transferred, a 0,1 variable for whether goods and warehouses are transferred, a random variable for the demand for goods in the warehouse, a mean parameter for the sales volume of goods in the warehouse, a variance parameter for the sales volume of goods in the warehouse, a minimum parameter for the sales volume of goods in the warehouse, and a maximum parameter for the sales volume of goods in the warehouse.

[0127] The calculation module 220 is used to construct a first model based on the data obtained by the input module 210 to determine the recommended allocation parameters of the goods, and then construct a second model based on the first model and the goods clearance parameters to determine the calibration allocation parameters of the goods, and obtain a third model by converting the second model, and obtain the calculation results of the calibration allocation parameters of the goods through the third model.

[0128] The output module 230 is used to output the calculation results of the goods calibration transfer parameters, and generate goods inventory transfer parameters according to the actual situation based on the calculation results of the goods calibration transfer parameters.

[0129] Figure 7 A block diagram of an electronic device according to an exemplary embodiment of the present application is shown.

[0130] like Figure 7 As shown, the electronic device 600 is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0131] like Figure 7 As shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc. The storage unit stores program code, which can be executed by the processing unit 610, so that the processing unit 610 performs the methods described in this specification according to various exemplary embodiments of the present application. For example, the processing unit 610 may perform the following Figure 1 The method shown in .

[0132] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0133] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0134] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0135] The electronic device 600 can also communicate with one or more external devices 700 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 via the bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0136] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. The technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal or a network device, etc.) to execute the method according to the embodiment of the present application.

[0137] The software product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0138] Computer-readable storage media may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0139] The program code for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0140] The computer-readable medium carries one or more programs. When the one or more programs are executed by the device, the computer-readable medium implements the aforementioned functions.

[0141] Those skilled in the art will appreciate that the modules described above can be distributed in the device according to the description of the embodiment, or can be modified accordingly to be used in one or more devices that are different from the embodiment. The modules of the above embodiment can be combined into one module or further divided into multiple submodules.

[0142] According to some embodiments of the present application, the technical solution of the present application considers and processes the uncertainty of goods sales factors with a distributed robust optimization method, eliminates the impact of goods sales factors on goods allocation parameters, and realizes the precise allocation of goods between upstream warehouses and downstream warehouses, so that problems in the production environment can be effectively solved and can be promoted and used on a large scale online.

[0143] The above embodiments of the present application are described in detail. The description of the above embodiments is only intended to help understand the method and core concept of the present application. At the same time, changes or modifications made by those skilled in the art based on the concept of the present application, the specific implementation methods and the scope of application of the present application, all fall within the scope of protection of the present application. In summary, the contents of this specification should not be understood as limiting the present application.

Claims

1. A method for adjusting the allocation of goods inventory, characterized in that: include: Step 1: Determine the recommended product transfer parameters, including: Obtaining a priority allocation weight coefficient for the product, wherein the priority allocation weight coefficient for the product includes: a first weight coefficient, which is a priority allocation weight coefficient for products that are greater than or equal to a preset first threshold; and a second weight coefficient, which is a priority allocation weight coefficient for products that are less than or equal to a preset second threshold; Constructing a first model according to the first weight coefficient and the second weight coefficient; Determining the recommended product transfer parameters using the first model and the constraints of the first model; The first model is expressed by the following formula ； in, is the first weight coefficient, is the second weight coefficient, and ， ； The constraints of the first model are ， ， ， ， ， ， ， in, I For product collection, J For warehouse collection, L ( l=(j 1, j 2)) is the line set, is the volume parameter of the goods, The number of items in a full box. Store parameters for goods in the warehouse, V j is the available volume parameter of the warehouse, is the parameter for the maximum number of pieces of goods transported on the trunk line, The maximum outbound shipment from the upstream warehouse SKU Width parameter, The target inventory level parameter for the goods in the warehouse. is the variable for the number of boxes of goods transferred, To store the goods in the downstream warehouse after the transfer, It is a 0,1 variable indicating whether the goods and routes are allocated. It is a 0,1 variable indicating whether the goods and warehouse are transferred; Step 2: Calibrate the recommended transfer parameters of the goods according to the preset goods clearance parameters to determine the calibrated transfer parameters of the goods, including: Determining the local fulfillment rate of the goods based on the goods clearance parameters; Constructing a second model based on the local fulfillment rate of the product and the first model; Determine the product calibration allocation parameter by using the second model and the fuzzy set of the second model; The second model is expressed by the following formula ， in, is the first weight coefficient in the second model, is the second weight coefficient in the second model, is the weight coefficient of the local satisfaction rate of the product in the second model; The fuzzy set of the second model is ， in, is the random variable of the demand for goods in the warehouse, is the mean parameter of the product’s sales volume in the warehouse, is the variance parameter of the product sales volume in the warehouse, is the minimum parameter for product sales in the warehouse, The maximum value parameter of the product’s sales volume in the warehouse; Step 3: Generate product inventory transfer parameters based on the product calibration transfer parameters, including: performing a dual transformation on the second model; Performing piecewise linear approximation on the second model after dual transformation to obtain a third model; Determining the calculation result of the goods calibration allocation parameter by the third model; The product inventory transfer parameters are generated according to the calculation results.

2. A device for adjusting the allocation of goods inventory, characterized in that: For executing the method according to claim 1, the allocation adjustment device comprises: An input module, which obtains data required for generating product suggestion transfer parameters and product calibration transfer parameters, as well as obtaining product clearance parameters; a calculation module, constructing a first model to determine the recommended goods transfer parameters, constructing a second model based on the first model and the goods clearance parameters to determine the goods calibration transfer parameters, and obtaining calculation results of the goods calibration transfer parameters by converting the second model; an output module, outputting the calculation result of the product calibration transfer parameter as a product inventory transfer parameter; The calculation module constructs a first model to determine the recommended product allocation parameters, including: Obtaining a priority allocation weight coefficient for the product, wherein the priority allocation weight coefficient for the product includes: a first weight coefficient, which is a priority allocation weight coefficient for products that are greater than or equal to a preset first threshold; and a second weight coefficient, which is a priority allocation weight coefficient for products that are less than or equal to a preset second threshold; Constructing a first model according to the first weight coefficient and the second weight coefficient; Determining the recommended product transfer parameters using the first model and the constraints of the first model; The first model is expressed by the following formula ； in, is the first weight coefficient, is the second weight coefficient, and ， ； The constraints of the first model are ， ， ， ， ， ， ， in, I For product collection, J For warehouse collection, L ( l=(j 1, j 2)) is the line set, is the volume parameter of the goods, The number of items in a full box. Store parameters for goods in the warehouse, V j is the available volume parameter of the warehouse, is the parameter for the maximum number of pieces of goods transported on the trunk line, The maximum outbound shipment from the upstream warehouse SKU Width parameter, The target inventory level parameter for the goods in the warehouse. is the variable for the number of boxes of goods transferred, To store the goods in the downstream warehouse after the transfer, It is a 0,1 variable indicating whether the goods and routes are allocated. A 0,1 variable indicating whether the goods and warehouse are transferred.

3. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method according to claim 1 .

4. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to claim 1 is implemented.

Images