CN113947341A - Supply chain replenishment method and device, computer equipment and storage medium - Google Patents

Supply chain replenishment method and device, computer equipment and storage medium Download PDF

Info

Publication number
CN113947341A
CN113947341A CN202010690412.8A CN202010690412A CN113947341A CN 113947341 A CN113947341 A CN 113947341A CN 202010690412 A CN202010690412 A CN 202010690412A CN 113947341 A CN113947341 A CN 113947341A
Authority
CN
China
Prior art keywords
product
replenishment
data
determining
products
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010690412.8A
Other languages
Chinese (zh)
Inventor
王晶
郭雨佳
柯俞嘉
许哲民
吕骥图
金虹希
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Shunrufenglai Technology Co ltd
Original Assignee
Shanghai Shunrufenglai Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Shunrufenglai Technology Co ltd filed Critical Shanghai Shunrufenglai Technology Co ltd
Priority to CN202010690412.8A priority Critical patent/CN113947341A/en
Publication of CN113947341A publication Critical patent/CN113947341A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Development Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Operations Research (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Analysis (AREA)
  • Computational Mathematics (AREA)
  • Game Theory and Decision Science (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Algebra (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application relates to a supply chain replenishment method, a supply chain replenishment device, computer equipment and a storage medium. The method comprises the following steps: acquiring actual product inventory data, product cost data, product geometric characteristic data and accommodating space data of a goods picking area in a supply chain; determining an initial function value, a circularly calculated function relation and a circular stopping condition for calculating the replenishment reference data based on the replenishment cost minimization principle and the space constraint of the sorting area; determining initial replenishment batches of products according to the initial function values, the product cost data, the product geometric characteristic data and the containing space data of the goods picking area; determining a final replenishment batch of the product and a final product reordering point according to the functional relation of the initial replenishment batch of the product and the cycle calculation and the cycle stop condition; and determining product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point of the products.

Description

Supply chain replenishment method and device, computer equipment and storage medium
Technical Field
The present application relates to the field of logistics technologies, and in particular, to a supply chain replenishment method and apparatus, a computer device, and a storage medium.
Background
In the application scene related to the warehousing supply chain, the warehousing area is divided into a storage area and a picking area, the storage area is a product storage area, the picking area is an area where pickers pack and deliver goods according to customer orders, and when the residual quantity of products in the picking area is insufficient, goods are required to be supplemented to the picking area from the storage area.
In the conventional replenishment scheme, warehouse personnel generally manually evaluate the replenishment situation of an article according to the historical sales volume of the article, such as whether replenishment is needed or not and the replenishment volume. However, with the change of the sales modes and the increasing abundance of the types of the articles, the replenishment quantity is predicted by warehouse personnel according to the historical sales quantity of the articles, so that the problem of low prediction accuracy of the replenishment quantity exists.
Disclosure of Invention
In view of the above, there is a need to provide a supply chain replenishment method, a supply chain replenishment device, a computer device and a storage medium, which can improve the accuracy of replenishment quantity prediction.
A supply chain restocking method, the method comprising:
acquiring actual product inventory data, product cost data, product geometric characteristic data and accommodating space data of a goods picking area in a supply chain;
determining an initial function value, a circularly calculated function relation and a circular stopping condition for calculating replenishment reference data based on a replenishment cost minimization principle and space constraints of the sorting area, wherein the replenishment reference data comprises product replenishment batches and product reordering points;
determining initial replenishment batches of products according to the initial function values, the product cost data, the product geometric characteristic data and the containing space data of the picking area;
determining a final replenishment batch of the product and a final product reordering point according to the initial replenishment batch of the product, the functional relation of the loop calculation and the loop stop condition;
and determining product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point.
In one embodiment, the determining the final replenishment lot size and the final product reordering point according to the functional relationship among the initial replenishment lot size of the product, the loop calculation and the loop stop condition comprises:
acquiring the product cost data, a first functional relation between the product geometric feature data and a correction coefficient and a second functional relation between the correction coefficient for circular calculation and the product geometric feature data;
obtaining currently circulating replenishment reference data according to the initial replenishment batch of the products and the first functional relation;
determining the correction coefficient of the next cycle according to the second functional relation;
obtaining replenishment reference data of the next cycle based on the correction coefficient of the next cycle and the first functional relation;
and when the difference value between the replenishment reference data of the next cycle and the replenishment reference data of the current cycle is smaller than a preset threshold value, taking the product replenishment batch of the next cycle as a final product replenishment batch, and taking a product reordering point of the next cycle as a final product reordering point.
In one embodiment, the obtaining of the replenishment reference data of the current cycle according to the initial replenishment batch of the product and the first functional relationship includes:
performing derivation processing on the first functional relation to obtain a third functional relation between product demand experience distribution data and the product replenishment batches;
obtaining a product demand experience distribution value of the current cycle according to the initial product replenishment batch and the third functional relation;
and determining a product reordering point of the current cycle based on the product demand experience distribution value of the current cycle.
In one embodiment, after determining the product reordering point of the current cycle based on the empirical distribution of product demand values of the current cycle, the method further includes:
performing derivation processing on the first functional relation to obtain a fourth functional relation between the product replenishment batch and the product cost data, the product geometric characteristic data and the correction coefficient;
determining product out-of-stock data according to the currently circulating product reordering points;
and obtaining the currently circulating product replenishment batch according to the product shortage data and the fourth functional relation.
In one embodiment, the determining the product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment lot of the products and the final product reordering point comprises:
when the actual inventory data of the products is smaller than or equal to the final product reordering point, the final product replenishment batch data is used as product replenishment data for replenishing goods from a storage area to the picking area in the supply chain;
and when the actual inventory data of the products is larger than the product reordering point, determining that the product replenishment data of replenishment from the storage area to the picking area in the supply chain is 0.
In one embodiment, the product cost data includes product cost to stock, product cost to stock out, and product cost to reorder.
In one embodiment, the product geometric feature data includes a product volume.
A supply chain restocking device, the device comprising:
the data acquisition module is used for acquiring actual inventory data, cost data and geometric characteristic data of products in a goods sorting area in a supply chain and accommodating space data of the goods sorting area;
the circular processing module is used for determining an initial function value, a circular calculated function relation and a circular stopping condition for calculating the replenishment reference data based on the replenishment cost minimization principle and the space constraint of the sorting area, wherein the replenishment reference data comprises a product replenishment batch and a product reordering point;
the first processing module is used for determining an initial replenishment batch of products according to the initial function value, the product cost data, the product geometric characteristic data and the containing space data of the picking area;
the second processing module is used for determining the final replenishment batch of the product and the final product reordering point according to the initial replenishment batch of the product, the functional relation of the loop calculation and the loop stop condition;
and the replenishment data determining module is used for determining the product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring actual product inventory data, product cost data, product geometric characteristic data and accommodating space data of a goods picking area in a supply chain;
determining an initial function value, a circularly calculated function relation and a circular stopping condition for calculating replenishment reference data based on a replenishment cost minimization principle and space constraints of the sorting area, wherein the replenishment reference data comprises product replenishment batches and product reordering points;
determining initial replenishment batches of products according to the initial function values, the product cost data, the product geometric characteristic data and the containing space data of the picking area;
determining a final replenishment batch of the product and a final product reordering point according to the initial replenishment batch of the product, the functional relation of the loop calculation and the loop stop condition;
and determining product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring actual product inventory data, product cost data, product geometric characteristic data and accommodating space data of a goods picking area in a supply chain;
determining an initial function value, a circularly calculated function relation and a circular stopping condition for calculating replenishment reference data based on a replenishment cost minimization principle and space constraints of the sorting area, wherein the replenishment reference data comprises product replenishment batches and product reordering points;
determining initial replenishment batches of products according to the initial function values, the product cost data, the product geometric characteristic data and the containing space data of the picking area;
determining a final replenishment batch of the product and a final product reordering point according to the initial replenishment batch of the product, the functional relation of the loop calculation and the loop stop condition;
and determining product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point.
The supply chain replenishment method, the supply chain replenishment device, the computer equipment and the storage medium are characterized in that the actual inventory data, the product cost data, the product geometric characteristic data and the accommodating space data of the goods picking area in the supply chain are obtained; determining an initial function value, a circularly calculated function relation and a circular stopping condition for calculating replenishment reference data based on a replenishment cost minimization principle and space constraint of a sorting area, wherein the replenishment reference data comprises product replenishment batches and product reordering points; determining initial replenishment batches of products according to the initial function values, the product cost data, the product geometric characteristic data and the containing space data of the goods picking area; determining a final replenishment batch of the product and a final product reordering point according to the functional relation of the initial replenishment batch of the product and the cycle calculation and the cycle stop condition; according to actual inventory data of products, final replenishment batch of products and final product reordering points, product replenishment data of replenishing goods from a storage area to a picking area in a supply chain is determined.
Drawings
FIG. 1 is a diagram of an exemplary implementation of a supply chain replenishment method;
FIG. 2 is a flow diagram illustrating a supply chain replenishment method according to one embodiment;
FIG. 3 is a schematic flow chart diagram illustrating the replenishment reference data determination step in one embodiment;
FIG. 4 is a block diagram of a supply chain replenishment mechanism in one embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The supply chain replenishment method provided by the application can be applied to the application environment shown in fig. 1. The user imports the actual inventory data, the product cost data, the product geometric characteristic data and the accommodating space data of the goods picking area in the supply chain into the mobile terminal through the mobile terminal. The method comprises the steps that a mobile terminal obtains actual product inventory data, product cost data, product geometric characteristic data and accommodating space data of a goods picking area in a supply chain; determining an initial function value, a circularly calculated function relation and a circular stopping condition for calculating replenishment reference data based on a replenishment cost minimization principle and space constraint of a sorting area, wherein the replenishment reference data comprises product replenishment batches and product reordering points; determining initial replenishment batches of products according to the initial function values, the product cost data, the product geometric characteristic data and the containing space data of the goods picking area; determining a final replenishment batch of the product and a final product reordering point according to the functional relation of the initial replenishment batch of the product and the cycle calculation and the cycle stop condition; and determining product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point of the products. The mobile terminal may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and the like. Specifically, the mobile terminal may execute the supply chain replenishment method in the embodiment of the present application through the processor.
In one embodiment, as shown in fig. 2, a supply chain replenishment method is provided, which is described by taking the example that the method is applied to the mobile terminal in fig. 1, and includes the following steps:
in step 202, actual inventory data, cost data, geometric characteristic data and accommodation space data of the picking area of the supply chain are obtained.
The actual inventory data of the product refers to the residual data of the product in the goods picking area of the warehouse, the product cost data refers to the data influencing the total cost in the goods replenishment period, and the product cost data specifically comprises the product goods holding cost, the product out-of-stock cost and the product re-ordering cost. The product geometry data may in particular comprise a product volume, and the receiving space data of the pick-up zone are used to identify the receiving space capacity of the pick-up zone.
And step 204, determining an initial function value, a function relation of loop calculation and a loop stop condition for calculating the replenishment reference data based on the replenishment cost minimization principle and the space constraint of the sorting area, wherein the replenishment reference data comprises the product replenishment batch and the product reordering point.
The principle of minimization of restocking costs and the space constraints of the picking area can be expressed in particular as:
Figure BDA0002589151570000081
wherein,
Figure BDA0002589151570000082
represents the expected total cost of goods for the ith product during the restocking cycle,
Figure BDA0002589151570000083
average order quantity, R, for ith producti-DiliIs the safe stock in the replenishment period of the ith product.
Figure BDA0002589151570000084
Indicating the expected reordering cost for the ith product during the replenishment cycle,
Figure BDA0002589151570000085
indicating the out-of-stock cost, Q, of the ith product during the restocking cyclei+RiRepresents the replenishment lot size of the ith product + the reorder point of the ith product, i.e., the maximum storage capacity of the pick zone; v. ofiRepresenting the volume of the ith product and V representing the allowed storage volume of the pick-up area.
And step 206, determining the initial replenishment batch of the products according to the initial function value, the product cost data, the product geometric characteristic data and the containing space data of the sorting area.
In particular, assume that
Figure BDA0002589151570000086
For replenishment in batches QiAnd (5) derivation to obtain:
Figure BDA0002589151570000087
let λ equal to 0, piIs equal to 0, to obtain
Figure BDA0002589151570000088
And step 208, determining the final replenishment batch of the product and the final product reordering point according to the functional relation of the initial replenishment batch of the product and the circulation calculation and the circulation stopping condition.
And according to the functional relation of the initial replenishment batch and the cycle calculation of the product and the cycle stop condition, carrying out cycle calculation solution on the replenishment reference data until the cycle stop condition is met, and at the moment, obtaining the final replenishment batch of the product and the final product reordering point.
And step 210, determining product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point.
After determining the replenishment batch and the re-ordering point of a certain product, comparing the inventory of the product in the sorting area with the re-ordering point of the product, and determining the replenishment quantity of the product for replenishment from the storage area to the sorting area according to the comparison result of the inventory of the product in the sorting area and the re-ordering point of the product. Specifically, determining the product replenishment data of replenishment from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point comprises the following steps: when the actual inventory data of the products is less than or equal to the product reordering point, the final replenishment batch of the products is used as the product replenishment data for replenishing goods from the storage area to the picking area in the supply chain; and when the actual inventory data of the product is larger than the final product reordering point, determining that the product replenishment data of replenishment from the storage area to the picking area in the supply chain is 0.
The supply chain replenishment method comprises the steps of obtaining actual product inventory data, product cost data, product geometric characteristic data and accommodating space data of a goods picking area in a supply chain; determining an initial function value, a circularly calculated function relation and a circular stopping condition for calculating replenishment reference data based on a replenishment cost minimization principle and space constraint of a sorting area, wherein the replenishment reference data comprises product replenishment batches and product reordering points; determining initial replenishment batches of products according to the initial function values, the product cost data, the product geometric characteristic data and the containing space data of the goods picking area; determining a final replenishment batch of the product and a final product reordering point according to the functional relation of the initial replenishment batch of the product and the cycle calculation and the cycle stop condition; according to actual inventory data of products, final replenishment batch of products and final product reordering points, product replenishment data of replenishing goods from a storage area to a picking area in a supply chain is determined.
In one embodiment, as shown in FIG. 3, determining the final restocking lot of products and the final restocking point of products according to the functional relationship of the initial restocking lot of products and the loop calculation and the loop stop condition comprises: step 302, acquiring product cost data, a first functional relation between product geometric characteristic data and a correction coefficient and a second functional relation between the correction coefficient and the product geometric characteristic data for circular calculation; 304, obtaining the currently circulating replenishment reference data according to the initial replenishment batch of the product and the first functional relation; step 306, determining the correction coefficient of the next cycle according to the second functional relation; 308, obtaining replenishment reference data of the next cycle based on the correction coefficient of the next cycle and the first functional relation; and step 310, when the difference value between the replenishment reference data of the next cycle and the replenishment reference data of the current cycle is smaller than a preset threshold value, taking the product replenishment batch of the next cycle as a product final replenishment batch, and taking the product re-ordering point of the next cycle as a product final re-ordering point. In particular, the second functionThe relationship can be expressed as:
Figure BDA0002589151570000101
Figure BDA0002589151570000102
wherein, λ represents a correction coefficient, α represents a learning rate, and is a constant defined by a user, and viIndicating the volume of the ith product. The first functional relationship may be expressed as
Figure BDA0002589151570000103
Figure BDA0002589151570000104
Wherein C (Q, R) represents the total cost in the replenishment cycle, viDenotes the volume of the ith product, QiIndicating a restocking lot of the ith product, RiIs the ith product reorder point and V represents the total allowed storage volume of the warehouse. Product cost data
Figure BDA0002589151570000105
DiIndicates the daily demand expectation, h, of the ith productiDenotes the shipment cost, p, of the ith productiIndicates the backorder cost of the ith product,/iIndicating the replenishment lead period, K, of the ith productiRepresents the reordering cost, n (R), of the ith producti) An expectation value representing the ith product shortage quantity.
In one embodiment, obtaining replenishment reference data of a current cycle according to an initial replenishment batch of a product and a first functional relationship comprises: performing derivation processing on the first functional relation to obtain a third functional relation between the product demand experience distribution data and the product replenishment batches; obtaining a product demand experience distribution value of the current cycle according to the initial replenishment batch of the product and the third functional relation; and determining a product reordering point of the current cycle based on the product demand experience distribution value of the current cycle. Specifically, after determining the product reordering point of the current cycle based on the empirical distribution value of the product demand of the current cycle, the method further includes: to the firstA functional relation is subjected to derivation processing to obtain a fourth functional relation between the product replenishment batch and the product cost data, the product geometric characteristic data and the correction coefficient; determining product out-of-stock data according to the currently circulating product reordering points; and obtaining the currently circulating product replenishment batch according to the product shortage data and the fourth functional relation. Derivation of the first functional relationship, i.e.
Figure BDA0002589151570000111
Respectively for replenishment batch QiAnd a reorder point RiDerivative to obtain
Figure BDA0002589151570000112
Let λ equal to 0, piIs equal to 0, to obtain
Figure BDA0002589151570000113
Will be provided with
Figure BDA0002589151570000114
Substitution into
Figure BDA0002589151570000115
Obtaining an empirical distribution function value F (R) corresponding to the product requirementi). Empirical distribution function value F (R) corresponding to product demandi) By passing
Figure BDA0002589151570000119
Calculating the Z value of the standard normal distribution; after the Z value of the product is obtained, the product reorder point R value is calculated from R ═ σ Z + μ. Based on the product reorder Point R value, by
Figure BDA0002589151570000116
Calculating the expected value of the number of the out-of-stock products; expected value based on the number of out-of-stock products and
Figure BDA0002589151570000117
calculating replenishment lot size Q of producti。
In one embodiment, the product replenishment lot size and the product reordering point can be determined through a replenishment model, product cost data, product geometric characteristic data and containing space data of the picking area are input into the replenishment model, and the product replenishment lot size and the product reordering point are obtained based on output data of the replenishment model. The goal of the restocking model is to minimize costs in the restocking cycle, including cost of goods in stock, cost of out of stock, and cost of purchase, and to meet warehouse space constraints.
Assuming that the demand distribution of the product is normally distributed, the parameters are defined as follows: diIndicates the daily demand expectation, h, of the ith SKU (Stock Keeping Unit)iCost of goods holding, unit representing the ith SKU: one element/one; p is a radical ofiStock out cost, unit, for the ith SKU: one element/one; liReplenishment lead time, unit, representing the ith SKU: day; kiRepresents the reordering cost for the ith SKU, in units: one element/one; beta is aiTarget order fulfillment rate, 0 ≦ β, for the ith SKUi≤1；n(Ri) An expectation value indicating the quantity of the ith SKU shortfall,
Figure BDA0002589151570000118
wherein R isiIs the reorder point for the ith SKU, μ is the mean demand for the pre-fill period, and σ is the standard deviation of demand for the pre-fill period. L is a standard loss function; qiRepresenting the replenishment lot size of the ith SKU, f (x) representing a demand distribution function of the replenishment lead period, and N representing the total number of SKUs; v. ofiVolume, unit representing the ith SKU: m is3(ii) a V represents the total allowed storage volume of the warehouse, unit: m is3(ii) a C (Q, R) represents the total cost in the restocking cycle.
The goal of the restocking model is to minimize the total cost in the restocking cycle and meet the warehouse space constraints, from which the expression of the restocking model is derived as follows:
Figure BDA0002589151570000121
wherein,
Figure BDA0002589151570000122
indicating the expected total cost of delivery of the ith SKU during the restocking cycle,
Figure BDA0002589151570000123
average order quantity, R, for ith SKUi-DiliIs the safe inventory within the replenishment period for the ith SKU.
Figure BDA0002589151570000124
Indicating the expected reordering cost for the ith SKU during the restocking cycle,
Figure BDA0002589151570000125
indicating the backorder cost, Q, of the ith SKU in the restocking cyclei+RiThe restocking lot representing the ith SKU + the reordering point, i.e., the maximum storage quantity, for the ith SKU.
And introducing a Lagrange multiplier to solve the expression of the replenishment model, and assuming that the Lagrange multiplier is lambda and the lambda is more than or equal to 0, obtaining:
Figure BDA0002589151570000126
above formula is respectively to replenishment in batches QiAnd a reorder point RiAnd (5) derivation to obtain:
Figure BDA0002589151570000127
Figure BDA0002589151570000128
Figure BDA0002589151570000129
Figure BDA00025891515700001210
wherein, F (R)i) And expressing an empirical distribution function corresponding to the product requirement.
The data processing flow of the replenishment model obtained by the method is as follows:
1. initializing L (Q, R), let λ be 0, piIs equal to 0, to obtain
Figure BDA0002589151570000131
2. Due to the fact that
Figure BDA0002589151570000132
Obtained by initialization
Figure BDA0002589151570000133
Substituting to obtain an empirical distribution function value F (R) corresponding to the product requirementi)。
3. Empirical distribution function value F (R) corresponding to product demandi) By zRi＝1-F(Ri) Calculating the Z value of the standard normal distribution; after the Z value of the product is obtained, according to the R ═ sigma ZR+ μ calculate the product reorder point R value.
4. Based on the product reorder Point R value, by
Figure BDA0002589151570000134
Calculating the expected value of the number of the out-of-stock products; expected value based on the number of out-of-stock products and
Figure BDA0002589151570000135
calculating replenishment lot size Q of producti。
5. According to
Figure BDA0002589151570000136
Updating lambda, wherein alpha represents the learning rate and is a constant defined by a user, repeatedly performing multi-round calculation on the product reordering points and the product replenishment batches until the product reordering points and the product replenishment batches obtained in one round and the product reordering points and the product replenishment batches obtained in the previous round are obtainedAnd when the difference value between the product replenishment batches is smaller than a preset value, stopping updating the iterative calculation of lambda, namely converging the product reordering point R and the product replenishment batch Q obtained by the calculation, wherein the space limit meets the requirement. If the iterative computation cannot be stopped, the space constraint may be too small to meet the target order fulfillment rate requirements.
It should be understood that although the various steps in the flow charts of fig. 2-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 4, there is provided a supply chain restocking device comprising: a data acquisition module 402, a loop processing module 404, a first processing module 406, a second processing module 408, and a replenishment data determination module 410. The data acquisition module 402 is configured to acquire actual inventory data of products in a sorting area, product cost data, geometric characteristic data of the products, and accommodation space data of the sorting area in a supply chain; a loop processing module 404, configured to determine an initial function value, a loop-calculated function relationship, and a loop stop condition for calculating a replenishment reference data based on a replenishment cost minimization principle and a space constraint of the sorting area, where the replenishment reference data includes a product replenishment batch and a product reordering point; the first processing module 406 is configured to determine an initial replenishment batch of products according to the initial function value, the product cost data, the product geometric feature data, and the accommodation space data of the sorting area; the second processing module 408 is configured to determine a final replenishment batch of the product and a final product reordering point according to the functional relationship between the initial replenishment batch of the product and the loop calculation and the loop stop condition; the replenishment data determining module 410 is configured to determine product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the product, the final replenishment lot size of the product, and the final product reordering point.
In one embodiment, the second processing module is further configured to obtain product cost data, a first functional relationship between the product geometric feature data and the correction coefficient, and a second functional relationship between the correction coefficient and the product geometric feature data for loop calculation; obtaining the currently circulating replenishment reference data according to the initial replenishment batch of the products and the first functional relation; determining the correction coefficient of the next cycle according to the second functional relation; obtaining replenishment reference data of the next cycle based on the correction coefficient of the next cycle and the first functional relation; and when the difference value between the replenishment reference data of the next cycle and the replenishment reference data of the current cycle is smaller than a preset threshold value, taking the product replenishment batch of the next cycle as a final product replenishment batch, and taking the product reordering point of the next cycle as a final product reordering point.
In one embodiment, the second processing module is further configured to perform derivation processing on the first functional relationship to obtain a third functional relationship between the product demand experience distribution data and the product replenishment batches; obtaining a product demand experience distribution value of the current cycle according to the initial replenishment batch of the product and the third functional relation; and determining a product reordering point of the current cycle based on the product demand experience distribution value of the current cycle.
In one embodiment, the second processing module is further configured to perform derivation processing on the first functional relationship to obtain a fourth functional relationship between the product replenishment batch and the product cost data, the product geometric characteristic data, and the correction coefficient; determining product out-of-stock data according to the currently circulating product reordering points; and obtaining the currently circulating product replenishment batch according to the product shortage data and the fourth functional relation.
In one embodiment, the replenishment data determination module is further used for taking the final replenishment batch data of the product as the replenishment data of the product in the supply chain, which is replenished to the picking area from the storage area when the actual inventory data of the product is less than or equal to the final product reordering point; and when the actual inventory data of the product is larger than the product reordering point, determining that the product replenishment data of replenishment from the storage area to the picking area in the supply chain is 0.
For the specific definition of the supply chain replenishment device, reference may be made to the above definition of the supply chain replenishment method, which is not described herein again. The various modules in the supply chain replenishment device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 5. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a supply chain replenishment method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring actual product inventory data, product cost data, product geometric characteristic data and accommodating space data of a goods picking area in a supply chain; determining an initial function value, a circularly calculated function relation and a circular stopping condition for calculating replenishment reference data based on a replenishment cost minimization principle and space constraint of a sorting area, wherein the replenishment reference data comprises product replenishment batches and product reordering points; determining initial replenishment batches of products according to the initial function values, the product cost data, the product geometric characteristic data and the containing space data of the goods picking area; determining a final replenishment batch of the product and a final product reordering point according to the functional relation of the initial replenishment batch of the product and the cycle calculation and the cycle stop condition; and determining product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point of the products.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring product cost data, a first functional relation between product geometric characteristic data and a correction coefficient and a second functional relation between the correction coefficient and the product geometric characteristic data for circular calculation; obtaining the currently circulating replenishment reference data according to the initial replenishment batch of the products and the first functional relation; determining the correction coefficient of the next cycle according to the second functional relation; obtaining replenishment reference data of the next cycle based on the correction coefficient of the next cycle and the first functional relation; and when the difference value between the replenishment reference data of the next cycle and the replenishment reference data of the current cycle is smaller than a preset threshold value, taking the product replenishment batch of the next cycle as a final product replenishment batch, and taking the product reordering point of the next cycle as a final product reordering point.
In one embodiment, the processor, when executing the computer program, further performs the steps of: performing derivation processing on the first functional relation to obtain a third functional relation between the product demand experience distribution data and the product replenishment batches; obtaining a product demand experience distribution value of the current cycle according to the initial replenishment batch of the product and the third functional relation; and determining a product reordering point of the current cycle based on the product demand experience distribution value of the current cycle.
In one embodiment, the processor, when executing the computer program, further performs the steps of: performing derivation processing on the first functional relation to obtain a fourth functional relation between the product replenishment batch and the product cost data, the product geometric characteristic data and the correction coefficient; determining product out-of-stock data according to the currently circulating product reordering points; and obtaining the currently circulating product replenishment batch according to the product shortage data and the fourth functional relation.
In one embodiment, the processor, when executing the computer program, further performs the steps of: when the actual inventory data of the product is less than or equal to the final product reordering point, the final product replenishment batch data is used as product replenishment data for replenishing goods from the storage area to the picking area in the supply chain; and when the actual inventory data of the product is larger than the product reordering point, determining that the product replenishment data of replenishment from the storage area to the picking area in the supply chain is 0.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring actual product inventory data, product cost data, product geometric characteristic data and accommodating space data of a goods picking area in a supply chain; determining an initial function value, a circularly calculated function relation and a circular stopping condition for calculating replenishment reference data based on a replenishment cost minimization principle and space constraint of a sorting area, wherein the replenishment reference data comprises product replenishment batches and product reordering points; determining initial replenishment batches of products according to the initial function values, the product cost data, the product geometric characteristic data and the containing space data of the goods picking area; determining a final replenishment batch of the product and a final product reordering point according to the functional relation of the initial replenishment batch of the product and the cycle calculation and the cycle stop condition; and determining product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point of the products.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring product cost data, a first functional relation between product geometric characteristic data and a correction coefficient and a second functional relation between the correction coefficient and the product geometric characteristic data for circular calculation; obtaining the currently circulating replenishment reference data according to the initial replenishment batch of the products and the first functional relation; determining the correction coefficient of the next cycle according to the second functional relation; obtaining replenishment reference data of the next cycle based on the correction coefficient of the next cycle and the first functional relation; and when the difference value between the replenishment reference data of the next cycle and the replenishment reference data of the current cycle is smaller than a preset threshold value, taking the product replenishment batch of the next cycle as a final product replenishment batch, and taking the product reordering point of the next cycle as a final product reordering point.
In one embodiment, the computer program when executed by the processor further performs the steps of: performing derivation processing on the first functional relation to obtain a third functional relation between the product demand experience distribution data and the product replenishment batches; obtaining a product demand experience distribution value of the current cycle according to the initial replenishment batch of the product and the third functional relation; and determining a product reordering point of the current cycle based on the product demand experience distribution value of the current cycle.
In one embodiment, the computer program when executed by the processor further performs the steps of: performing derivation processing on the first functional relation to obtain a fourth functional relation between the product replenishment batch and the product cost data, the product geometric characteristic data and the correction coefficient; determining product out-of-stock data according to the currently circulating product reordering points; and obtaining the currently circulating product replenishment batch according to the product shortage data and the fourth functional relation.
In one embodiment, the computer program when executed by the processor further performs the steps of: when the actual inventory data of the product is less than or equal to the final product reordering point, the final product replenishment batch data is used as product replenishment data for replenishing goods from the storage area to the picking area in the supply chain; and when the actual inventory data of the product is larger than the product reordering point, determining that the product replenishment data of replenishment from the storage area to the picking area in the supply chain is 0.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A supply chain restocking method, the method comprising:
acquiring actual product inventory data, product cost data, product geometric characteristic data and accommodating space data of a goods picking area in a supply chain;
determining an initial function value, a circularly calculated function relation and a circular stopping condition for calculating replenishment reference data based on a replenishment cost minimization principle and space constraints of the sorting area, wherein the replenishment reference data comprises product replenishment batches and product reordering points;
determining initial replenishment batches of products according to the initial function values, the product cost data, the product geometric characteristic data and the containing space data of the picking area;
determining a final replenishment batch of the product and a final product reordering point according to the initial replenishment batch of the product, the functional relation of the loop calculation and the loop stop condition;
and determining product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point.
2. The method of claim 1, wherein determining a final restocking lot of products and a final reordering point of products based on the initial restocking lot of products, the loop-calculated functional relationship, and the loop-stop condition comprises:
acquiring the product cost data, a first functional relation between the product geometric feature data and a correction coefficient and a second functional relation between the correction coefficient for circular calculation and the product geometric feature data;
obtaining currently circulating replenishment reference data according to the initial replenishment batch of the products and the first functional relation;
determining the correction coefficient of the next cycle according to the second functional relation;
obtaining replenishment reference data of the next cycle based on the correction coefficient of the next cycle and the first functional relation;
and when the difference value between the replenishment reference data of the next cycle and the replenishment reference data of the current cycle is smaller than a preset threshold value, taking the product replenishment batch of the next cycle as a final product replenishment batch, and taking a product reordering point of the next cycle as a final product reordering point.
3. The method of claim 2, wherein obtaining replenishment reference data for a current cycle according to the initial replenishment batch of products and the first functional relationship comprises:
performing derivation processing on the first functional relation to obtain a third functional relation between product demand experience distribution data and the product replenishment batches;
obtaining a product demand experience distribution value of the current cycle according to the initial product replenishment batch and the third functional relation;
and determining a product reordering point of the current cycle based on the product demand experience distribution value of the current cycle.
4. The method of claim 3, wherein after determining the product reorder point for the current cycle based on the empirical distribution of product demand values for the current cycle, further comprising:
performing derivation processing on the first functional relation to obtain a fourth functional relation between the product replenishment batch and the product cost data, the product geometric characteristic data and the correction coefficient;
determining product out-of-stock data according to the currently circulating product reordering points;
and obtaining the currently circulating product replenishment batch according to the product shortage data and the fourth functional relation.
5. The method of claim 1, wherein determining product restocking data for restocking from a storage area to the picking area in the supply chain based on the actual inventory data of the products, the final restocking lot of the products, and the final reordering point of the products comprises:
when the actual inventory data of the products is smaller than or equal to the final product reordering point, the final product replenishment batch data is used as product replenishment data for replenishing goods from a storage area to the picking area in the supply chain;
and when the actual inventory data of the products is larger than the product reordering point, determining that the product replenishment data of replenishment from the storage area to the picking area in the supply chain is 0.
6. The method of any one of claims 1 to 5, wherein the product cost data includes product cost to stock, product cost to out stock, and product cost to reorder.
7. The method of any one of claims 1 to 5, wherein the product geometry data comprises a product volume.
8. A supply chain restocking device, the device comprising:
the data acquisition module is used for acquiring actual inventory data, cost data and geometric characteristic data of products in a goods sorting area in a supply chain and accommodating space data of the goods sorting area;
the circular processing module is used for determining an initial function value, a circular calculated function relation and a circular stopping condition for calculating the replenishment reference data based on the replenishment cost minimization principle and the space constraint of the sorting area, wherein the replenishment reference data comprises a product replenishment batch and a product reordering point;
the first processing module is used for determining an initial replenishment batch of products according to the initial function value, the product cost data, the product geometric characteristic data and the containing space data of the picking area;
the second processing module is used for determining the final replenishment batch of the product and the final product reordering point according to the initial replenishment batch of the product, the functional relation of the loop calculation and the loop stop condition;
and the replenishment data determining module is used for determining the product replenishment data for replenishing goods from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final replenishment batch of the products and the final product reordering point.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
CN202010690412.8A 2020-07-17 2020-07-17 Supply chain replenishment method and device, computer equipment and storage medium Pending CN113947341A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010690412.8A CN113947341A (en) 2020-07-17 2020-07-17 Supply chain replenishment method and device, computer equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010690412.8A CN113947341A (en) 2020-07-17 2020-07-17 Supply chain replenishment method and device, computer equipment and storage medium

Publications (1)

Publication Number Publication Date
CN113947341A true CN113947341A (en) 2022-01-18

Family

ID=79327016

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010690412.8A Pending CN113947341A (en) 2020-07-17 2020-07-17 Supply chain replenishment method and device, computer equipment and storage medium

Country Status (1)

Country Link
CN (1) CN113947341A (en)

Citations (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070027745A1 (en) * 2005-07-28 2007-02-01 Sap Ag System and method of assortment, space, and price optimization in retail store
US20070083413A1 (en) * 2005-10-11 2007-04-12 Oracle International Corporation Allocating inventory levels
US20120004944A1 (en) * 2007-05-18 2012-01-05 Logistics Management Institute (Lmi) Method and computer system for settng inventory control levels from demand inter-arrival time, demand size statistics
CN103400252A (en) * 2013-08-02 2013-11-20 华迪计算机集团有限公司 Goods inventory management method and device
CN104680240A (en) * 2013-12-03 2015-06-03 冯卫星 Supply chain inventory optimization method for lightweight information sharing
CN104732287A (en) * 2013-12-19 2015-06-24 广州市地下铁道总公司 Stock control method based on optimum replenishment period of spare part
CN104820913A (en) * 2015-04-24 2015-08-05 北京京东尚科信息技术有限公司 Replenishment method and apparatus
CN107563705A (en) * 2017-09-25 2018-01-09 四川长虹电器股份有限公司 Household electrical appliances product safety stock and the system and method ordered goods again are analyzed using big data
US20180025316A1 (en) * 2011-12-15 2018-01-25 Jda Software Group, Inc. System and Method of Simultaneous Computation of Optimal Order Point and Optimal Order Quantity
CN108985691A (en) * 2018-07-11 2018-12-11 北京实派科技有限公司 A kind of automatic replenishing method and system based on dynamic stock control
CN110046761A (en) * 2019-04-11 2019-07-23 北京工业大学 A kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle
CN110046739A (en) * 2019-01-18 2019-07-23 创新奇智(南京)科技有限公司 Replenishing method and device based on multistage sales volume forecast of distribution
CN110163546A (en) * 2019-05-20 2019-08-23 四川长虹电器股份有限公司 A method of optimal replenishment quantity is determined based on big data
CN110348635A (en) * 2019-07-12 2019-10-18 创新奇智(成都)科技有限公司 Intelligent replenishing method, storage medium, system and device based on end-to-end study
CN110363454A (en) * 2018-04-09 2019-10-22 杉数科技(北京)有限公司 For determining the method and device of commodity replenishment quantity
CN110390449A (en) * 2018-04-17 2019-10-29 北京京东尚科信息技术有限公司 Warehouse replenishing method and device
CN110610289A (en) * 2019-08-05 2019-12-24 石化盈科信息技术有限责任公司 Method for predicting finished oil supply quantity of gas station, computer storage medium and computer equipment
CN110826831A (en) * 2018-08-13 2020-02-21 北京京东振世信息技术有限公司 Method and device for restocking a picking zone of a warehouse
CN110852668A (en) * 2019-09-23 2020-02-28 北京旷视机器人技术有限公司 Goods warehousing processing method and device, warehousing system and computer equipment
CN111126903A (en) * 2019-12-13 2020-05-08 江苏苏宁物流有限公司 Replenishment method, device and system
CN111222815A (en) * 2018-11-27 2020-06-02 顺丰科技有限公司 Method, device and equipment for determining single item replenishment and storage medium
CN111242524A (en) * 2018-11-29 2020-06-05 顺丰科技有限公司 Method, system, equipment and storage medium for determining replenishment quantity of single article
CN111325490A (en) * 2018-12-14 2020-06-23 顺丰科技有限公司 Replenishment method and device

Patent Citations (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070027745A1 (en) * 2005-07-28 2007-02-01 Sap Ag System and method of assortment, space, and price optimization in retail store
US20070083413A1 (en) * 2005-10-11 2007-04-12 Oracle International Corporation Allocating inventory levels
US20120004944A1 (en) * 2007-05-18 2012-01-05 Logistics Management Institute (Lmi) Method and computer system for settng inventory control levels from demand inter-arrival time, demand size statistics
US20180025316A1 (en) * 2011-12-15 2018-01-25 Jda Software Group, Inc. System and Method of Simultaneous Computation of Optimal Order Point and Optimal Order Quantity
CN103400252A (en) * 2013-08-02 2013-11-20 华迪计算机集团有限公司 Goods inventory management method and device
CN104680240A (en) * 2013-12-03 2015-06-03 冯卫星 Supply chain inventory optimization method for lightweight information sharing
CN104732287A (en) * 2013-12-19 2015-06-24 广州市地下铁道总公司 Stock control method based on optimum replenishment period of spare part
CN104820913A (en) * 2015-04-24 2015-08-05 北京京东尚科信息技术有限公司 Replenishment method and apparatus
CN107563705A (en) * 2017-09-25 2018-01-09 四川长虹电器股份有限公司 Household electrical appliances product safety stock and the system and method ordered goods again are analyzed using big data
CN110363454A (en) * 2018-04-09 2019-10-22 杉数科技(北京)有限公司 For determining the method and device of commodity replenishment quantity
CN110390449A (en) * 2018-04-17 2019-10-29 北京京东尚科信息技术有限公司 Warehouse replenishing method and device
CN108985691A (en) * 2018-07-11 2018-12-11 北京实派科技有限公司 A kind of automatic replenishing method and system based on dynamic stock control
CN110826831A (en) * 2018-08-13 2020-02-21 北京京东振世信息技术有限公司 Method and device for restocking a picking zone of a warehouse
CN111222815A (en) * 2018-11-27 2020-06-02 顺丰科技有限公司 Method, device and equipment for determining single item replenishment and storage medium
CN111242524A (en) * 2018-11-29 2020-06-05 顺丰科技有限公司 Method, system, equipment and storage medium for determining replenishment quantity of single article
CN111325490A (en) * 2018-12-14 2020-06-23 顺丰科技有限公司 Replenishment method and device
CN110046739A (en) * 2019-01-18 2019-07-23 创新奇智(南京)科技有限公司 Replenishing method and device based on multistage sales volume forecast of distribution
CN110046761A (en) * 2019-04-11 2019-07-23 北京工业大学 A kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle
CN110163546A (en) * 2019-05-20 2019-08-23 四川长虹电器股份有限公司 A method of optimal replenishment quantity is determined based on big data
CN110348635A (en) * 2019-07-12 2019-10-18 创新奇智(成都)科技有限公司 Intelligent replenishing method, storage medium, system and device based on end-to-end study
CN110610289A (en) * 2019-08-05 2019-12-24 石化盈科信息技术有限责任公司 Method for predicting finished oil supply quantity of gas station, computer storage medium and computer equipment
CN110852668A (en) * 2019-09-23 2020-02-28 北京旷视机器人技术有限公司 Goods warehousing processing method and device, warehousing system and computer equipment
CN111126903A (en) * 2019-12-13 2020-05-08 江苏苏宁物流有限公司 Replenishment method, device and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
时兵兵等: "焊管厂成品库库存优化研究", 北京石油化工学院学报, vol. 24, no. 3, 15 September 2016 (2016-09-15), pages 52 - 56 *
李群霞 等: "基于模糊集的可部分补货库存模型的研究", 物流技术, no. 19, 31 December 2010 (2010-12-31), pages 43 - 46 *

Similar Documents

Publication Publication Date Title
US11983727B2 (en) System and method for data-driven insight into stocking out-of-stock shelves
CN111242524B (en) Method, system, equipment and storage medium for determining single product replenishment quantity
CN112561414B (en) Inventory management method, apparatus and computer readable storage medium
CN110852668A (en) Goods warehousing processing method and device, warehousing system and computer equipment
CN110633820B (en) Warehouse address recommendation method and device and computer readable storage medium
CN110390449B (en) Warehouse replenishment method and device
CN111401619A (en) Purchase order processing method and device, electronic equipment and storage medium
CN111126903A (en) Replenishment method, device and system
CN114240304B (en) Warehouse inventory control method and device, storage medium and ERP system
US11727348B2 (en) System and method for self-tuning optimization of an item replenishment policy
CN110046761A (en) A kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle
CN113627846A (en) Inventory adjusting method and device, electronic equipment and computer readable medium
CN111815198A (en) Method, device and equipment for replenishing goods in store
CN108108933A (en) Position distribution method of storing in a warehouse and device
CN112633803A (en) Access library management and replenishment method, device and storage medium
KR102473656B1 (en) Electronic logistics management system and logistics management method to manage safety inventory
CN113947341A (en) Supply chain replenishment method and device, computer equipment and storage medium
CN111401636B (en) Method and system for realizing intelligent replenishment of chain merchants
CN114186903A (en) Warehouse product selection method and device, computer equipment and storage medium
CN116993261A (en) Automatic warehouse cargo space optimization method and system based on remora optimization algorithm
KR101898762B1 (en) Apparatus for distributing goods with considering demand uncertainty, method thereof, and computer recordable medium storing the method
CN113919764A (en) Method and device for determining in-bin replenishment quantity, computer equipment and storage medium
CN113837706B (en) Method, device, equipment and medium for controlling inventory of articles
CN112990827A (en) Cargo data processing method, device, equipment and storage medium
CN114065018A (en) Commodity popularity determination method and device, electronic equipment and computer readable storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination