CN107563702B - Commodity storage allocation method, device and storage medium - Google Patents
Commodity storage allocation method, device and storage medium Download PDFInfo
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Abstract
The invention discloses a commodity storage allocation method, a device and a storage medium, wherein the method comprises the following steps: acquiring a first association degree between commodities according to historical commodity purchasing data; acquiring a second association degree between the commodity and the target warehouse based on the historical commodity purchasing data and the commodity inventory information; screening out the fused commodities with the quantity being the fused quantity of the commodities from the commodities according to a preset commodity fusion rule and based on the first relevance degree and the second relevance degree; and storing the screened fused commodities in a target warehouse. The method, the device and the storage medium of the invention realize the great reduction of the order splitting rate, reduce the splitting times after the order of the client is issued through a small amount of commodities with multiple warehouses for stock, reduce the workload of producing the order, improve the packaging efficiency, reduce the logistics operation cost, simultaneously reduce the increase of the packaging materials caused by the order splitting, lay the foundation for the green and environment-friendly operation and improve the experience degree of the client.
Description
Technical Field
The invention relates to the technical field of electronic commerce, in particular to a commodity storage allocation method, a commodity storage allocation device and a storage medium.
Background
At present, in order to pursue good customer timeliness experience, e-commerce establishes a warehouse for full-quantity commodity stock. Due to the fact that the commodities are various in types and wide in commodity coverage, one warehouse or one logistics park cannot meet the requirements. E-commerce generally adopts a warehouse built based on classification of goods, for example: daily sales products are usually in one or more warehouses; 3C digital computer products are stored in one or more warehouses; the apparel is in one or more warehouses. Due to the class expansion and the free freight of order taking of the customer, the same order of the customer often contains commodities of multiple classes, and the commodities are distributed in different warehouses, so that the order is split.
The existing methods for reducing the bill splitting rate have two types: the first method is to find the association degree between each single commodity, as shown in fig. 1, and determine the adhesion degree between the SKU and the SKU of the commodity by analyzing the historical orders, wherein the thicker the lines, the higher the association degree, so as to place the SKUs with high adhesion degree in a same warehouse, and reduce the order splitting rate. The second method is to search the association relation between the commodities with high sales volume and the single bin on the basis of commodity association degree and analyze the set association between the commodities and the single bin. As shown in FIG. 2, the X axis represents the number of shared products, the Y axis represents the theoretical order splitting rate between two bins, and the more the number of shared products, the lower the theoretical order splitting rate, but the marginal utility is decreased. For example, if the order splitting rate needs to be reduced to 10%, 2000 commodities need to be selected to be shared and fused with the class bin. The two existing methods have the defects of lack of flexibility, possibility that the same commodity is placed in a plurality of bins, poor improvement effect of reducing the bill splitting rate and the like.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method, an apparatus and a storage medium for merchandise warehousing and distribution.
According to one aspect of the present invention, there is provided a method for warehousing and allocating goods, comprising: acquiring a first association degree between commodities according to historical commodity purchasing data; determining a target warehouse, and acquiring a second association degree between the commodity and the target warehouse based on historical commodity purchasing data and commodity inventory information; setting the fusion quantity of commodities, and screening out the fusion commodities with the quantity equal to the fusion quantity of the commodities from the commodities according to a preset commodity fusion rule and based on the first correlation degree and the second correlation degree; and storing the screened fusion commodities in the target warehouse.
Optionally, the screening, according to a preset commodity fusion rule and based on the first association degree and the second association degree, fused commodities of which the quantity is the commodity fusion quantity from the commodities includes: constructing a commodity fusion graph, wherein the commodity and the target warehouse are nodes in the commodity fusion graph respectively; establishing edges connecting the nodes in the commodity fusion graph based on the first relevance and the second relevance, and endowing the edges with corresponding weight values; acquiring a maximum weight connected subgraph from the commodity fusion graph according to a preset subgraph selection condition and the weight value, wherein nodes in the maximum weight connected subgraph comprise: and the target warehouse nodes and the fused commodity nodes with the quantity being the commodity fused quantity.
Optionally, the obtaining a maximum weight connected subgraph from the commodity fusion graph according to a preset subgraph selection condition and the weight value includes: calculating fusion commodity weight value sigmai,j∈IZij*WijAcquiring a maximum weight connected subgraph according to the weight value of the fused commodity and the subgraph selection condition; wherein the subgraph selection condition comprises: sigmai,j∈IZij*WijThe value of (d) is a maximum value; i and j are respectively nodes in the commodity fusion graph, I is a set of all nodes in the commodity fusion graph, and Z isijIdentifying whether an edge, W, connecting node i and node j is selectedijIs the weight value corresponding to the edge connecting node i and node j.
Alternatively, if node i and node j are both commodity nodes, then W is determinedij＝Lij*(Ni+Nj) (ii) a Wherein L isijIs a first degree of association, N, between the goods represented by node i and node jiPredicted purchase quantity, N, for the goods represented by node ijA predicted purchase quantity for the item represented by node j; if node i is a commodity node and node j is a target warehouse node, then W is determinedij＝Mij*Nj(ii) a Wherein M isijIs a second degree of association between the item represented by node i and the target warehouse.
Optionally, the obtaining a first association degree between associated commodities according to historical purchase data includes: acquiring the order quantity D of purchased commodities represented by the node i based on commodity purchase ordersiAnd the number D of orders of the products represented by the purchase node jjAnd the number D of orders for which the products represented by the node i and the node j are purchased simultaneouslyij(ii) a Determining a first degree of association between the goods represented by node i and node j
Optionally, a second degree of association M is determinedijIs the sum of the first association degrees between the commodities represented by the node i and all other commodities currently stored in the target warehouse.
Optionally, the subgraph selection condition includes: constraint conditions for determining the fusion commodity node; wherein the constraint condition comprises: the commodity fusion quantity and the fusion commodity node comprise the target warehouse.
Optionally, the constraint further includes: and the warehouse mutual exclusion restriction constraint that two or more commodities cannot be put into the same warehouse at the same time and the warehouse capacity restriction of the target warehouse.
Optionally, the screened information of the fused product and the target warehouse information are sent to a product storage function system, where the product storage function system includes: replenishment system, allotment system, stock warehouse system.
According to another aspect of the present invention, there is provided a merchandise warehousing and distribution device, comprising: the first association degree acquisition module is used for acquiring first association degrees among commodities according to historical commodity purchase data; the second association degree acquisition module is used for determining a target warehouse and acquiring a second association degree between the commodity and the target warehouse based on historical commodity purchasing data and commodity inventory information; and the fusion commodity processing module is used for setting the fusion quantity of commodities, screening fusion commodities with the quantity being the fusion quantity of the commodities from the commodities according to a preset commodity fusion rule and based on the first relevance degree and the second relevance degree, and storing the screened fusion commodities in the target warehouse.
Optionally, the fused merchandise processing module includes: the model building unit is used for building a commodity fusion graph, wherein the commodity and the target warehouse are nodes in the commodity fusion graph respectively; establishing edges connecting the nodes in the commodity fusion graph based on the first relevance and the second relevance, and endowing the edges with corresponding weight values; a fused commodity determining unit, configured to obtain a maximum weight connected subgraph from the commodity fused graph according to a preset subgraph selection condition and the weight value, where a node in the maximum weight connected subgraph includes: and the target warehouse nodes and the fused commodity nodes with the quantity being the commodity fused quantity.
Optionally, the fused commodity determining unit is configured to calculate a fused commodity weight value Σi,j∈IZij*WijAcquiring a maximum weight connected subgraph according to the weight value of the fused commodity and the subgraph selection condition; wherein the subgraph selection condition comprises: sigmai,j∈IZij*WijThe value of (d) is a maximum value; i and j are respectively nodes in the commodity fusion graph, I is a set of all nodes in the commodity fusion graph, and Z isijIdentifying whether an edge, W, connecting node i and node j is selectedijIs the weight value corresponding to the edge connecting node i and node j.
Optionally, the fused commodity determining unit is configured to determine W if node i and node j are both commodity nodesij＝Lij*(Ni+Nj) (ii) a Wherein L isijIs a first degree of association, N, between the goods represented by node i and node jiPredicted purchase quantity, N, for the goods represented by node ijA predicted purchase quantity for the item represented by node j; the fused commodity determining unit is used for determining W if the node i is a commodity node and the node j is a target warehouse nodeij＝Mij*Nj(ii) a Wherein M isijIs a second degree of association between the item represented by node i and the target warehouse.
Optionally, the first association obtaining module is configured to obtain, based on the commodity purchase order, an order quantity D of the commodities represented by the purchased node iiAnd the number D of orders of the products represented by the purchase node jjAnd the number D of orders for which the products represented by the node i and the node j are purchased simultaneouslyij(ii) a Determining a first degree of association between the goods represented by node i and node j
Optionally, the second association obtaining module is configured to determine a second association MijIs the sum of the first association degrees between the commodities represented by the node i and all other commodities currently stored in the target warehouse.
Optionally, the subgraph selection condition includes: constraint conditions for determining the fusion commodity node; wherein the constraint condition comprises: the commodity fusion quantity and the fusion commodity node comprise the target warehouse.
Optionally, the constraint further includes: and the warehouse mutual exclusion restriction constraint that two or more commodities cannot be put into the same warehouse at the same time and the warehouse capacity restriction of the target warehouse.
Optionally, the fused merchandise processing module includes: the fusion commodity information sending unit is used for sending the screened information of the fusion commodity and the target warehouse information to a commodity storage function system, wherein the commodity storage function system comprises: replenishment system, allotment system, stock warehouse system.
According to another aspect of the present invention, there is provided a merchandise warehousing and distribution device, comprising: a memory; and a processor coupled to the memory, the processor configured to execute the method of merchandise warehousing deployment as described above based on instructions stored in the memory.
According to still another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions, and the instructions are executed by a processor to implement the commodity warehousing deployment method as described above.
According to the commodity storage allocation method, the device and the storage medium, the fused commodities are screened from the commodities and stored in the target warehouse according to the correlation degree between the commodities and the target warehouse, so that the order splitting rate is greatly reduced, the splitting times after the order of a customer is issued are reduced through a small quantity of commodities and multiple warehouses for stock, the workload of production orders is reduced, the packaging efficiency is improved, and the logistics operation cost is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic illustration of the adhesion between articles of the prior art;
FIG. 2 is a diagram illustrating the correspondence between the number of shares and the rate of order splitting in the prior art;
FIG. 3 is a flow chart of an embodiment of a method for warehousing goods according to the invention;
FIG. 4 is a schematic diagram illustrating establishment of a merchandise fusion chart according to another embodiment of the merchandise warehousing deployment method of the invention;
FIG. 5 is a diagram illustrating the correspondence between the number of the fused commodities and the order splitting rate in another embodiment of the commodity warehousing and distribution method according to the invention;
FIG. 6 is a block diagram of an embodiment of a merchandise warehousing and distribution system according to the invention;
FIG. 7 is a block diagram of a merged merchandise handling module in an embodiment of the merchandise warehousing and distribution device according to the invention;
FIG. 8 is a block diagram of another embodiment of a merchandise warehousing and distribution system according to the invention.
Detailed Description
The present invention now will be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention are shown. The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first", "second", and the like are used hereinafter only for descriptive distinction and not for other specific meanings.
Fig. 3 is a flowchart of an embodiment of a merchandise warehousing allocation method according to the invention, as shown in fig. 3:
The first degree of association may be calculated in various ways. For example, a first degree of association between SKUs (Stock Keeping units) of the commodity is calculated as: for items a and B, assuming that the number of orders for purchasing a is 100, the number of orders for purchasing B is 200, and the number of orders for purchasing a and B is 50, the degree of association between a and B is 50/(100+ 200).
And 102, determining a target warehouse, and acquiring a second association degree between the commodity and the target warehouse based on the historical purchasing data of the commodity and the inventory information of the commodity.
The second degree of association may be calculated in various ways. For example, the second degree of association between the SKU of the item and the warehouse is equal to the sum of the first degrees of association of the SKU of the item and the SKUs of all other items in the warehouse. The degree of association between the warehouses may be the sum of the first degrees of association between the SKUs of all of the items in both warehouses. For example, the commodities of warehouse 1 have a and B, and the commodities of warehouse 2 have C and D, and the association degree between warehouse 1 and warehouse 2 is the sum of the first association degrees between a and C, A and D, B and C, B and D.
And 103, setting the fusion quantity of the commodities, and screening out the fusion commodities with the quantity being the fusion quantity of the commodities from the commodities according to a preset commodity fusion rule and based on the first relevance degree and the second relevance degree. The commodity fusion rules can be various and can be set according to specific requirements.
And 104, storing the screened fused commodities in a target warehouse.
The fusion commodities are stored in the same warehouse, the information of the screened fusion commodities and the information of the target warehouse are sent to a commodity storage function system for commodity feeding, allocation and the like, and the commodity storage function system comprises: a replenishment system, a transfer system, a stock warehouse system and the like.
Based on the commodity warehousing and allocation method in the embodiment, the splitting times after the orders of the customers are issued can be reduced through the multi-bin stock of the commodities, the workload of the production orders is reduced, the packaging efficiency is improved, the logistics operation cost is reduced, and the packaging increase caused by splitting is also reduced.
In one embodiment, there may be a plurality of methods for screening the number of the fused commodities equal to the commodity fusion number. For example, a commodity fusion graph is constructed, and the commodity and the target warehouse are nodes in the commodity fusion graph respectively. And establishing edges of the connection nodes in the commodity fusion graph based on the first relevance and the second relevance, and endowing the edges with corresponding weight values. And acquiring a maximum weight connected subgraph from the commodity fusion graph according to preset subgraph selection conditions and weight values, wherein nodes in the maximum weight connected subgraph comprise target warehouse nodes and fusion commodity nodes with the quantity being the commodity fusion quantity.
There are various methods for obtaining the maximum weight connected subgraph from the commodity fusion graph, for example, an integer programming method may be adopted. Integer programming refers to the programming in which variables are limited (in whole or in part) to integers, and if in a linear model variables are limited to integers, it is called integer linear programming, and the programming can be subdivided into linear, quadratic and nonlinear integer programming from the construction of constraints.
For example, calculate the fused product weight value ∑i,j∈IZij*WijAnd acquiring a maximum weight connected subgraph according to the weight value of the fused commodities and the subgraph selection condition. The subgraph selection condition comprises the following steps: sigmai,j∈IZij*WijThe value of (a) is the maximum value, I and j are respectively the nodes in the commodity fusion graph, I is the set of all nodes in the commodity fusion graph, ZijIdentifying whether an edge, W, connecting node i and node j is selectedijIs the weight value corresponding to the edge connecting node i and node j.
If the node i and the node j are commodity nodes, determining Wij＝Lij*(Ni+Nj)，LijIs a first degree of association, N, between the goods represented by node i and node jiPredicted purchase quantity, N, for the goods represented by node ijIs the predicted purchase quantity of the item represented by node j. The predicted purchase amount may be the amount of shipment in a week, month, etc. If node i is a commodity node and node j is a target warehouse node, then W is determinedij＝Mij*Nj，MijIs a second degree of association between the item represented by node i and the target warehouse.
The order quantity D of the purchased goods represented by the node i can be obtained based on the goods purchase orderiAnd the number D of orders of the products represented by the purchase node jjAnd the number D of orders for which the products represented by the node i and the node j are purchased simultaneouslyij. Determining a first degree of association between the goods represented by node i and node j
A second degree of association M may be determinedijWhich is the sum of the first degrees of association between the goods represented by the node i and all other goods currently stored in the target warehouse.
The subgraph selection conditions comprise constraint conditions used for determining the fused commodity nodes, and the constraint conditions comprise commodity fusion quantity, the fused commodity nodes comprise target warehouses and the like. The constraints may further include: the storage mutual exclusion restriction that two or more commodities cannot be put into the same warehouse at the same time, the storage capacity restriction of a target warehouse, and the like.
In one embodiment, the association between SKUs of the good, and between SKUs of the good and the warehouse is calculated using big data correlation techniques based on historical orders. According to the business requirements, the warehouse which has a large influence on reducing the bill disassembling rate can be automatically screened out and used as the warehouse which needs to be fused. And after the warehouse needing to be fused is determined, automatically selecting the SKUs of the corresponding quantity of commodities and the target warehouse for fusing according to the counted association degree by using an integer programming technology.
A commodity fusion graph can be established according to the relation between the SKU and the warehouse of the commodity, the node is the SKU or the warehouse of the commodity, the side weight is the association degree between the two nodes, and the aim is to select a maximum weight connected subgraph to enable the sum of the association degrees of the sides in the maximum weight connected subgraph to be maximum, namely the reduced sub-orders are maximum.
For example, there are two warehouses H1 and H2, with 5 commodity SKUs S1、S2、S3、S4、S5. The goal of the merchandising order is to select 3 merchandise SKUs to place in either H1 or H2 so that the order splitting rate is reduced the most. H1 is first set as the target warehouse, and I is set to { H1, S ═1、S2、S3、S4、S5}。
Setting a constraint condition:
constraint 1:
zij≥yi+yj-1；
constraint 2:
zij≤yi；
constraint 3:
zij≤yj；
constraint 4: sigmai∈{S1,S2,S3,S4,S5}yi≤3；
Constraint 5: y isH1＝1；
Constraint 6:
0≤yi≤1,0≤Zij≤1。
the constraints 1-3 described above are used to limit nodes and their corresponding edges to being selected or not selected at the same time. Constraint 4 denotesThe constant 3 of the constraint 4 may be changed to another number by selecting 3 nodes at most. Constraint 5 indicates that the corresponding warehouse H1 must be selected. The constraint 6 limits all variables to values other than 0, i.e. 1. Yi represents whether inode is selected, and constraints 1,2, and 3 are configurable. Calculating the maximized fusion commodity weight value sigma according to the 6 constraint conditionsi,j∈IZij*WijThen, 3 fused commodities are selected, and the selected fused commodities are stored in the warehouse H1.
In one embodiment, as shown in FIG. 4, a commodity fusion graph is constructed, with node A representing a bin and nodes b, c, d representing 3 commodities. I ═ a, b, c, d, and W _ ij represents the weight on the edge, e.g., W _ Ab ═ 10. Each edge in the commodity fusion graph corresponds to a variable z _ ij, and the value is 0 or 1, which indicates whether the edge is selected. A variable y _ i is associated with each node to indicate whether the node is selected.
The fusion target is to select the maximum weight connected subgraph so that the sum of weights on its edges is maximum. If the maximum number of SKUs limiting the selected item is 2 (i.e. the constant after constraint 4 is changed to 2), the solution results in y _ a ═ y _ c ═ y _ d ═ 1, and y _ b ═ 0; z _ Ab ═ z _ bc ═ z _ Ac ═ 0, z _ cd ═ z _ Ad ═ z _ Ac ═ 1, i.e., the commodities c and d were selected and stored in warehouse a as a fused commodity.
In one embodiment, by using the method for warehousing and allocating commodities of the present invention, association between SKUs of commodities and between SKUs and warehouses is considered comprehensively, various constraint conditions can be configured flexibly, such as mutually exclusive relationship between warehouse capacity and SKUs, SKU ratio limit, etc., so that fewer SKUs can participate in fusion, and the rate of order removal can be reduced greatly, as shown in fig. 5, 1000 commodities are selected as fused commodities by using the method for warehousing and allocating commodities of the present invention, and the rate of order removal can be reduced by 20%.
In one embodiment, as shown in fig. 6, the present invention provides a merchandise warehousing deployment device 60, comprising: a first association degree obtaining module 61, a second association degree obtaining module 62, and a fused commodity processing module 63. The first association degree obtaining module 61 obtains a first association degree between commodities according to historical purchase data of the commodities. The second association degree obtaining module 62 determines a target warehouse, and obtains a second association degree between the commodity and the target warehouse based on the historical purchase data of the commodity and the inventory information of the commodity. The fused commodity processing module 63 sets the fused quantity of commodities, screens out fused commodities of which the quantity is the fused quantity of commodities from the commodities according to a preset commodity fusion rule and based on the first relevance degree and the second relevance degree, and stores the screened fused commodities in a target warehouse.
As shown in fig. 7, the fused commodity processing module 63 includes: a model building unit 631, a fused commodity determining unit 632, and a fused commodity information transmitting unit 633. The model building unit 631 constructs a commodity fusion graph, and the commodity and the target warehouse are nodes in the commodity fusion graph respectively. The model building unit 631 builds edges of the connection nodes in the product fusion graph based on the first relevance and the second relevance, and assigns corresponding weight values to the edges. The fused commodity determining unit 632 obtains a maximum weight connected subgraph from the commodity fused graph according to a preset subgraph selecting condition and a weight value, wherein nodes in the maximum weight connected subgraph include: the target warehouse nodes and the fusion commodity nodes with the quantity being the commodity fusion quantity.
The fused commodity determining unit 632 calculates a fused commodity weight value Σi,j∈IZij*WijAcquiring a maximum weight connected subgraph according to the weight value of the fused commodity and a subgraph selection condition, wherein the subgraph selection condition comprises the following steps: sigmai,j∈IZij*WijThe value of (d) is a maximum value; i and j are respectively nodes in the commodity fusion graph, I is a set of all nodes in the commodity fusion graph, and ZijIdentifying whether an edge, W, connecting node i and node j is selectedijIs the weight value corresponding to the edge connecting node i and node j.
If both the node i and the node j are commodity nodes, the fused commodity determining unit 632 determines Wij＝Lij*(Ni+Nj)，LijIs a first degree of association, N, between the goods represented by node i and node jiPredicted purchase quantity, N, for the goods represented by node ijIs the predicted purchase quantity of the item represented by node j. If the node i is a commodity node and the node j is a target warehouseNode, the fusion commodity determination unit 632 determines Wij＝Mij*Nj，MijIs a second degree of association between the item represented by node i and the target warehouse.
The first association degree acquisition module 61 acquires the order quantity D of purchased products represented by the node i based on the product purchase orderiAnd the number D of orders of the products represented by the purchase node jjAnd the number D of orders for which the products represented by the node i and the node j are purchased simultaneouslyijDetermining a first degree of association between the goods represented by the node i and the node j
The second association degree obtaining module 62 determines a second association degree MijWhich is the sum of the first degrees of association between the goods represented by the node i and all other goods currently stored in the target warehouse.
FIG. 8 is a block diagram of another embodiment of a merchandise warehousing and distribution system according to the invention. As shown in fig. 8, the apparatus may include a memory 81, a processor 82, a communication interface 83, and a bus 84. The memory 81 is used for storing instructions, the processor 82 is coupled to the memory 81, and the processor 82 is configured to execute the commodity warehousing deployment method based on the instructions stored in the memory 81.
The memory 81 may be a high-speed RAM memory, a non-volatile memory (non-volatile memory), or the like, and the memory 81 may be a memory array. The storage 81 may also be partitioned and the blocks may be combined into virtual volumes according to certain rules. The processor 82 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement the warehouse allocation method of the present invention.
In one embodiment, the present invention provides a computer-readable storage medium storing computer instructions, which when executed by a processor implement the method for deploying goods warehousing as described in any of the above embodiments.
The commodity storage allocation method, the commodity storage allocation device and the storage medium provided by the embodiment can screen out the fused commodities from the commodities and store the fused commodities in the target warehouse according to the association degrees between the SKUs of the commodities and between the SKUs and the target warehouse, can flexibly configure various constraint conditions, greatly reduce the order splitting rate, reduce the splitting times after the order of a customer is issued through a small quantity of commodities and multiple warehouse stock, reduce the workload of producing the order, improve the packaging efficiency, reduce the logistics operation cost, simultaneously reduce the increase of packages caused by splitting the order, lay a foundation for green operation, and improve the experience degree of the customer.
The method and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless specifically indicated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (18)
1. A commodity storage allocation method is characterized by comprising the following steps:
acquiring a first association degree between commodities according to historical commodity purchasing data;
determining a target warehouse, and acquiring a second association degree between the commodity and the target warehouse based on the historical commodity purchasing data and the commodity inventory information;
setting the fusion quantity of commodities, and screening out the fusion commodities with the quantity equal to the fusion quantity of the commodities from the commodities according to a preset commodity fusion rule and based on the first correlation degree and the second correlation degree;
constructing a commodity fusion graph, wherein the commodities and the target warehouse are nodes in the commodity fusion graph respectively; establishing edges connecting the nodes in the commodity fusion graph based on the first relevance and the second relevance, and endowing the edges with corresponding weight values;
according to a preset subgraph selection condition and the weight value, obtaining a maximum weight connected subgraph from the commodity fusion graph, wherein nodes in the maximum weight connected subgraph comprise: the target warehouse nodes and the fusion commodity nodes with the quantity being the commodity fusion quantity;
and storing the screened fusion commodities in the target warehouse.
2. The method of claim 1, wherein the obtaining a maximum weight connected subgraph from the product fusion graph according to a preset subgraph selection condition and the weight value comprises:
calculating fusion commodity weight value sigmai,j∈IZij*WijAcquiring a maximum weight connected subgraph according to the weight value of the fused commodity and the subgraph selection condition;
wherein the subgraph selection condition comprises: sigmai,j∈IZij*WijThe value of (d) is a maximum value; i and j are respectively nodes in the commodity fusion graph, I is a set of all nodes in the commodity fusion graph, and Z isijIdentifying whether an edge, W, connecting node i and node j is selectedijIs the weight value corresponding to the edge connecting node i and node j.
3. The method of claim 2, further comprising:
if the node i and the node j are commodity nodes, determining Wij＝Lij*(Ni+Nj)；
Wherein L isijIs a first degree of association, N, between the goods represented by node i and node jiPredicted purchase quantity, N, for the goods represented by node ijA predicted purchase quantity for the item represented by node j;
if node i is a commodity node and node j is a target warehouse node, then W is determinedij＝Mij*Nj；
Wherein M isijIs a second degree of association between the item represented by node i and the target warehouse.
4. The method of claim 3, wherein obtaining a first degree of association between items based on historical purchase data for the items comprises:
acquiring the order quantity D of purchased commodities represented by the node i based on commodity purchase ordersiAnd the number D of orders of the products represented by the purchase node jjAnd the number D of orders for which the products represented by the node i and the node j are purchased simultaneouslyij；
Determining a first degree of association between the goods represented by node i and node j
5. The method of claim 3, further comprising:
determining a second degree of association MijIs the sum of the first association degrees between the commodities represented by the node i and all other commodities currently stored in the target warehouse.
6. The method of claim 2,
the subgraph selection condition comprises the following steps: constraint conditions for determining the fusion commodity node;
wherein the constraint condition comprises: the commodity fusion quantity and the fusion commodity node comprise the target warehouse.
7. The method of claim 6,
the constraint further comprises: and the warehouse mutual exclusion restriction constraint that two or more commodities cannot be put into the same warehouse at the same time and the warehouse capacity restriction of the target warehouse.
8. The method of claim 1, further comprising:
sending the screened information of the fused commodity and the target warehouse information to a commodity storage function system, wherein the commodity storage function system comprises: replenishment system, allotment system, stock warehouse system.
9. A merchandise warehousing deployment device, comprising:
the first association degree acquisition module is used for acquiring first association degrees among commodities according to historical commodity purchase data;
the second association degree acquisition module is used for determining a target warehouse and acquiring a second association degree between the commodity and the target warehouse based on historical commodity purchasing data and commodity inventory information;
the fusion commodity processing module is used for setting the fusion quantity of commodities, screening fusion commodities with the fusion quantity of the commodities from the commodities according to a preset commodity fusion rule and based on the first relevance degree and the second relevance degree, and storing the screened fusion commodities in the target warehouse;
the fusion commodity processing module comprises:
the model building unit is used for building a commodity fusion graph, and the commodity and the target warehouse are nodes in the commodity fusion graph respectively; establishing edges connecting the nodes in the commodity fusion graph based on the first relevance and the second relevance, and endowing the edges with corresponding weight values;
a fused commodity determining unit, configured to obtain a maximum weight connected subgraph from the commodity fused graph according to a preset subgraph selection condition and the weight value, where a node in the maximum weight connected subgraph includes: and the target warehouse nodes and the fused commodity nodes with the quantity being the commodity fused quantity.
10. The apparatus of claim 9,
the fused commodity determining unit is used for calculating the weighted value sigma of the fused commodityi,j∈IZij*WijAcquiring a maximum weight connected subgraph according to the weight value of the fused commodity and the subgraph selection condition; wherein the subgraph selection condition comprises: sigmai,j∈ IZij*WijThe value of (d) is a maximum value; i and j are respectively nodes in the commodity fusion graph, I is a set of all nodes in the commodity fusion graph, and Z isijIdentifying whether an edge, W, connecting node i and node j is selectedijIs the weight value corresponding to the edge connecting node i and node j.
11. The apparatus of claim 10,
the fused commodity determining unit is used for determining W if the node i and the node j are commodity nodesij＝Lij*(Ni+Nj) (ii) a Wherein L isijIs a first degree of association, N, between the goods represented by node i and node jiPredicted purchase quantity, N, for the goods represented by node ijA predicted purchase quantity for the item represented by node j;
the fused commodity determining unit is used for determining W if the node i is a commodity node and the node j is a target warehouse nodeij＝Mij*Nj(ii) a Wherein M isijIs a second degree of association between the item represented by node i and the target warehouse.
12. The apparatus of claim 11,
the first association degree obtaining module is configured to obtain, based on a commodity purchase order, an order quantity D of purchased commodities represented by the node iiAnd the number D of orders of the products represented by the purchase node jjAnd the number D of orders for which the products represented by the node i and the node j are purchased simultaneouslyij(ii) a Determining a first degree of association between the goods represented by node i and node j
13. The apparatus of claim 11,
the second association degree obtaining module is used for determining a second association degree MijIs the sum of the first association degrees between the commodities represented by the node i and all other commodities currently stored in the target warehouse.
14. The apparatus of claim 10,
the subgraph selection condition comprises the following steps: constraint conditions for determining the fusion commodity node;
wherein the constraint condition comprises: the commodity fusion quantity and the fusion commodity node comprise the target warehouse.
15. The apparatus of claim 14,
the constraint further comprises: and the warehouse mutual exclusion restriction constraint that two or more commodities cannot be put into the same warehouse at the same time and the warehouse capacity restriction of the target warehouse.
16. The apparatus of claim 9,
the fusion commodity processing module comprises:
the fusion commodity information sending unit is used for sending the screened information of the fusion commodity and the target warehouse information to a commodity storage function system, wherein the commodity storage function system comprises: replenishment system, allotment system, stock warehouse system.
17. A merchandise warehousing deployment device, comprising:
a memory; and
a processor coupled to the memory, the processor configured to execute the merchandise warehousing deployment method of any of claims 1-8 based on instructions stored in the memory.
18. A computer-readable storage medium storing computer instructions which, when executed by a processor, implement the method according to any one of claims 1 to 8.
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| CN110059991B (en) * | 2018-01-18 | 2024-01-16 | 北京京东尚科信息技术有限公司 | Warehouse item selection method, system, electronic device and computer readable medium |
| CN110472899B (en) * | 2018-05-09 | 2021-11-30 | 北京京东振世信息技术有限公司 | Method and device for distributing articles out of warehouse |
| CN110969378A (en) * | 2018-09-28 | 2020-04-07 | 北京京东尚科信息技术有限公司 | Warehouse article configuration method and device |
| CN109615460A (en) * | 2018-11-09 | 2019-04-12 | 北京京东尚科信息技术有限公司 | Gather the selection method and selection system of single commodity |
| CN109656540B (en) * | 2018-11-16 | 2022-07-26 | 浙江心怡供应链管理有限公司 | Replenishment stock space recommendation method based on Apriori algorithm |
| CN111340578A (en) * | 2018-12-18 | 2020-06-26 | 北京京东尚科信息技术有限公司 | Method, device, medium and electronic equipment for generating commodity association relationship |
| CN109711778B (en) * | 2018-12-26 | 2023-05-30 | 南方科技大学 | Inventory allocation method, device and storage medium of warehouse network |
| CN110197309B (en) * | 2019-06-05 | 2021-11-26 | 北京极智嘉科技股份有限公司 | Order processing method, device, equipment and storage medium |
| CN110298615B (en) * | 2019-06-26 | 2022-02-08 | 阿里巴巴（中国）有限公司 | Method, apparatus, medium, and computing device for selecting items in a warehouse |
| CN110705744B (en) * | 2019-08-26 | 2022-10-21 | 南京苏宁加电子商务有限公司 | Planogram generation method, planogram generation apparatus, computer device, and storage medium |
| CN110689299B (en) * | 2019-09-06 | 2022-09-20 | 北京旷视机器人技术有限公司 | Method for determining replenishment shelf, storage management device, system, equipment and medium |
| CN112633541A (en) * | 2019-09-24 | 2021-04-09 | 北京沃东天骏信息技术有限公司 | Inventory network optimization method and device based on single commodity flow |
| CN110648103B (en) * | 2019-09-29 | 2023-04-07 | 阿里巴巴（中国）有限公司 | Method, apparatus, medium, and computing device for selecting items in a warehouse |
| CN112785214A (en) * | 2019-11-07 | 2021-05-11 | 北京沃东天骏信息技术有限公司 | Method, device and storage medium for optimizing inventory |
| CN113139767A (en) * | 2020-01-20 | 2021-07-20 | 菜鸟智能物流控股有限公司 | Logistics object allocation method and device, electronic equipment and computer-readable storage medium |
| CN111738657B (en) * | 2020-06-19 | 2022-11-04 | 合肥工业大学 | Logistics bill splitting optimization method and system based on substitute recommendation |
| CN111985999B (en) * | 2020-08-21 | 2024-03-05 | 北京京东振世信息技术有限公司 | Method, system, device and storage medium for realizing warehouse order picking |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106921504A (en) * | 2015-12-24 | 2017-07-04 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of the associated path for determining different user |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106156880A (en) * | 2015-04-22 | 2016-11-23 | 阿里巴巴集团控股有限公司 | A kind of predict the method for inventory allocation ratio, device and electronic equipment |
| CN106327116A (en) * | 2015-07-09 | 2017-01-11 | 阿里巴巴集团控股有限公司 | Method and device for carrying out regional inventory allocation on target articles |
| CN106384219B (en) * | 2016-10-13 | 2021-09-07 | 北京京东振世信息技术有限公司 | Storage sub-warehouse auxiliary analysis method and device |
| CN106709692A (en) * | 2017-02-24 | 2017-05-24 | 北京远大宏略科技股份有限公司 | Logistics center storage position allocation method |
-
2017
- 2017-09-14 CN CN201710824611.1A patent/CN107563702B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106921504A (en) * | 2015-12-24 | 2017-07-04 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of the associated path for determining different user |
Non-Patent Citations (1)
| Title |
|---|
| 《多属性无向加权图上的聚类方法研究》;陈登峰;《中国优秀硕士学位论文全文数据库（电子期刊）信息科技辑》;20120615;全文 * |
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