CN112633541A - Inventory network optimization method and device based on single commodity flow - Google Patents

Inventory network optimization method and device based on single commodity flow Download PDF

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CN112633541A
CN112633541A CN201910904944.4A CN201910904944A CN112633541A CN 112633541 A CN112633541 A CN 112633541A CN 201910904944 A CN201910904944 A CN 201910904944A CN 112633541 A CN112633541 A CN 112633541A
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戚永志
陈磊
李朝峰
郑德鹏
谭格帆
夏雨晴
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Beijing Wodong Tianjun Information Technology Co Ltd
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Abstract

The invention discloses a method and a device for optimizing an inventory network based on single commodity flow, and relates to the technical field of warehouse logistics. One embodiment of the method comprises: constructing an inventory network model according to the node types in the inventory network and the fulfillment relationship among the nodes; constructing an objective function according to the constraint conditions and the inventory network model, wherein the constraint conditions comprise single item flow constraints; and solving the objective function to realize the optimization of the inventory network. The implementation method can greatly improve the universality and the flexibility of the inventory network model and the solving speed of the objective function, and obviously improve the optimization effect of the inventory network.

Description

Inventory network optimization method and device based on single commodity flow
Technical Field
The invention relates to the technical field of warehouse logistics, in particular to a method and a device for optimizing an inventory network based on single commodity flow.
Background
With the explosion of online sales, it is important to improve the rationality and scientificity of SKU (Stock Keeping Unit) storage quantity, which is a core problem of supply chain in inventory network.
Inventory layout is a typical inventory network optimization problem, and the inventory layout in the prior art is based on a heuristic method and operation optimization to realize an inventory network with a warehouse as a network node.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
1. because the number (integer) of the warehouse is used as an optimization variable, the solving speed is low when the mixed integer algorithm is adopted for carrying out optimization solving; when the heuristic algorithm is adopted for solving, although the solving speed can be improved, the optimization effect is poor;
2. the current inventory network model has poor expandability and universality, and is difficult to adapt to different constraints of different merchants and scenes with more required levels of the inventory network.
Disclosure of Invention
In view of this, embodiments of the present invention provide an inventory network optimization method and apparatus based on a single commodity flow, which can greatly improve the universality and flexibility of an inventory network model and the solving speed of an objective function, and significantly improve the optimization effect of an inventory network.
To achieve the above object, according to a first aspect of the embodiments of the present invention, there is provided an inventory network optimization method based on singles flow, including:
constructing an inventory network model according to the node types in the inventory network and the fulfillment relationship among the nodes;
constructing an objective function according to a constraint condition and an inventory network model, wherein the constraint condition comprises a single item flow constraint;
and solving the objective function to realize the optimization of the inventory network.
Further, the inventory network model includes three matrices, and the step of constructing the inventory network model includes: and obtaining a fulfillment relation matrix of the inventory network according to the node types in the inventory network and the fulfillment relation among the nodes, and determining a time efficiency matrix and a cost matrix of the inventory network according to the fulfillment relation matrix.
Further, the node types include: the system comprises a purchasing layer node, a middle layer node and a demand layer node.
Further, the constraint condition further includes: demand constraints, aging constraints, and sales fluctuation conductance constraints.
Further, solving the result includes: optimizing the number of nodes and optimizing the number of inventory items for the nodes.
Further, if the solution result is a non-integer, the inventory network optimization method further includes: and carrying out rounding operation on the solved result.
According to a second aspect of the embodiments of the present invention, there is provided an inventory network optimization device based on singles flow, including:
the inventory network model building module is used for building an inventory network model according to the node types in the inventory network and the performance relation among the nodes;
the system comprises an objective function construction module, a storage network model generation module and a data processing module, wherein the objective function construction module is used for constructing an objective function according to constraint conditions and the storage network model, and the constraint conditions comprise single commodity flow constraints;
and the solving module is used for solving the objective function so as to realize the optimization of the inventory network.
Further, the inventory network model includes three matrices, and the inventory network model building module is further configured to: and obtaining a fulfillment relation matrix of the inventory network according to the node types in the inventory network and the fulfillment relation among the nodes, and determining a time efficiency matrix and a cost matrix of the inventory network according to the fulfillment relation matrix.
According to a third aspect of the embodiments of the present invention, there is provided a terminal, including:
one or more processors;
a storage device for storing one or more programs,
when executed by one or more processors, cause the one or more processors to implement any of the methods for singles-flow-based inventory network optimization described above.
According to a fourth aspect of embodiments of the present invention, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements any of the methods for singles-flow based inventory network optimization described above.
One embodiment of the above invention has the following advantages or benefits: because the inventory network model is constructed according to the node types in the inventory network and the performance relation among the nodes; constructing an objective function according to a constraint condition and an inventory network model, wherein the constraint condition comprises a single item flow constraint; the technical means of solving the objective function to realize the optimization of the inventory network overcomes the technical problems of poor expandability and universality of an inventory network model, low solving speed of the objective function and poor optimization effect in the prior art, thereby greatly improving the universality and flexibility of the inventory network model and the solving speed of the objective function and remarkably improving the optimization effect of the inventory network.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a main flow of a singleton-based inventory network optimization method according to a first embodiment of the present invention;
FIG. 2a is a schematic diagram of a main flow of a singleton-based inventory network optimization method according to a second embodiment of the present invention;
fig. 2b is a fulfillment relationship matrix Aij of an inventory network provided in accordance with a second embodiment of the present invention.
FIG. 3 is a schematic diagram of the main modules of a singles-flow based inventory network optimization apparatus, according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 5 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
FIG. 1 is a schematic diagram of a main flow of a singleton-based inventory network optimization method according to a first embodiment of the present invention; as shown in fig. 1, the inventory network optimization method based on singleton flow provided by the embodiment of the present invention includes:
and step S101, constructing an inventory network model according to the node types in the inventory network and the performance relationship among the nodes.
The storage is taken as the inventory node in the inventory network, the storage is divided into different types according to the performance/the assumed function of the inventory node, and then a multistage inventory network model can be constructed according to actual needs according to the performance relation among different nodes.
Specifically, according to a specific implementation manner of the embodiment of the present invention, the node types include a purchasing layer node, an intermediate layer node and a demand layer node.
Purchasing a layer node: and the node is positioned at the upstream of the external supply network and the internal supply network of the inventory network, is used for purchasing various single products and can generate purchasing cost, and plays a role in supplying the single products to the middle layer and the demand layer. Such as a Central Distribution Center (CDC). The node of the purchasing layer has only one layer, and the number of the nodes of each layer can be multiple.
Intermediate layer nodes: and the node is positioned in the midstream of the inventory network and is used for receiving the single products supplied by the purchasing layer node and performing direct performance on a client or performing support relationship with a next-level inventory node (another intermediate layer node or a demand layer node), and only generates inventory cost and transportation and distribution cost without purchasing cost. Such as a Regional Distribution Center (RDC), a Front Distribution Center (FDC), etc. The nodes in the middle layer can be divided into a plurality of layers according to the level of fulfillment, and the number of the nodes in each layer can be multiple.
A demand layer node: and the node which is positioned at the downstream of the inventory network, receives the single products supplied by the middle layer node and directly faces the customer requirement, and mainly refers to a quantity Aggregated Place (SAP). The demand layer node is only one layer, but the number of nodes of the layer can be more.
Therefore, a multi-layer fulfillment inventory network model is constructed according to the three node types and the fulfillment relationship among the nodes, and the problem that the inventory network model in the prior art is poor in universality and only suitable for two-level network modeling is solved. It should be noted that, if the two-level inventory network model is adopted, the model only needs to be constructed according to the nodes of the purchase layer and the demand layer.
Further, according to an embodiment of the present invention, the inventory network model includes three matrices, and the step of constructing the inventory network model includes: firstly, obtaining a fulfillment relation matrix of the inventory network according to the node types in the inventory network and fulfillment relations among the nodes, and then determining a time-efficiency matrix and a cost matrix of the inventory network according to the fulfillment relation matrix, namely completing the construction of an inventory network model.
Specifically, the node numbers of the purchasing layer node, the intermediate layer node and the demand layer node are counted, and a fulfillment relationship matrix of the inventory network is obtained according to the fulfillment support relationship among the nodes, wherein in the fulfillment relationship matrix Aij,
a row node i represents a node (a purchasing layer node and a middle layer node) capable of supplying a single item in the inventory network, a column node j represents a node (a purchasing layer node, a middle layer node and a demand layer node) capable of receiving a single item in the inventory network, an element Aij in a fulfillment relation matrix Aij is 1, the node i can support the node j, namely, a fulfillment relation exists between the node i and the node j, and the node i can supply the single item to the node j or the node j cannot receive the single item supplied by the node i; aij being 0 indicates that node i may not support node j, i.e., there is no fulfillment relationship between node i and node j, and node i may not supply node j with the item. It should be noted that, in the inventory network, since a fulfillment relationship indirectly exists between any two nodes, the support relationship (fulfillment relationship) refers to a direct fulfillment relationship.
Further, after the fulfillment relationship matrix of the inventory network is obtained, the time-efficiency matrix and the cost matrix of the multi-layer fulfillment inventory network are determined based on the allowable support relationships (fulfillment relationship) among various nodes represented in the fulfillment relationship matrix. Specifically, if there is a direct support relationship (direct performance), that is, aij is 1, the corresponding positions of the aging matrix and the cost matrix are filled with the corresponding performance required aging and performance required cost, where the performance aging is the time required for completing one delivery from node i to node j; the performance cost refers to the delivery cost required to complete the delivery of an item from node i to node j. If there is no direct support relationship (indirect performing), i.e. aij is 0, a large number is filled in the corresponding positions of the aging matrix and the cost matrix (e.g. the corresponding position of the aging matrix is filled in for 9999 days, and the corresponding position of the cost matrix is filled in for 9999 yuan), where the purpose of filling the large number is only to indicate that there is no direct performing relationship between the two nodes, i.e. there is a disconnection between the two nodes in the inventory network. Similarly, the above meaning may be indicated by filling in english letters or mathematical symbols.
The multi-level fulfillment inventory network model constructed by the method can process a multi-level fulfillment inventory network and complex inventory network support relations, such as cross-level support, peer-level support and the like, can flexibly calculate different cost items (purchase cost, delivery cost, inventory cost and the like) aiming at different node types, is beneficial to reducing the difficulty of subsequently establishing an objective function according to cost constraint, further improves the solving speed and improves the optimization effect.
And S102, constructing an objective function according to the constraint conditions and the inventory network model, wherein the constraint conditions comprise single item flow constraints.
Based on the inventory network model, an objective function is constructed in combination with constraints, the objective function being constructed so as to minimize the sum of various types of costs, i.e., to minimize the total cost in the entire inventory network. The single item flow is an optimization variable of the objective function, and the single item flow is restricted to be that the number of the single items circulating between two nodes with a performance relation is larger than zero. An objective function constructed based on the inventory network model is optimized based on SKU flow, the distribution sizes of the SKU flow on different fulfillment support paths are different, when the SKU is zero, the fulfillment support path is indicated to be unavailable, namely, the fulfillment support relationship indicated by the path is not used during actual fulfillment; when the total sum of the total SKU flows flowing into a certain node is zero, the inventory node is indicated not to store the SKU, the starting condition of the inventory node can be judged through the distribution of the SKU flows, and then the optimization of the inventory node in the inventory network is realized.
Specifically, the calculable costs mainly include: distribution costs, procurement costs, and inventory costs.
Distribution cost: in the whole inventory network, the total distribution cost of the single items circulating between the inventory nodes with direct fulfillment relationship exists, and the distribution cost between the two nodes with fulfillment relationship is calculated according to the fulfillment cost of the single item and the number of the fulfillment item flows;
and (3) purchasing cost: the cost generated by purchasing the single item on the purchasing layer is calculated according to the purchasing price, the service level, the mean value of the lead time and the sales fluctuation.
Inventory cost: the cost generated by storing the single products by the inventory nodes (the purchasing layer node, the middle layer node and the demand layer node) is calculated according to the service level, the average value of the lead time, the sales fluctuation and the capital occupation cost.
Further, the constraint condition further includes: demand constraints, aging constraints, and sales fluctuation conductance constraints.
And (3) requirement constraint: meaning that the sum of the received singlets for the demand layer is equal to the total demand for the demand layer. The requirement constraint is met, the inventory cost of a demand layer is greatly reduced, the distribution cost of redundant single products is greatly reduced, and the optimization effect of the inventory layout is further improved.
And (4) time efficiency constraint: the requirement of each type of performance time efficiency requires that a certain sales volume ratio is achieved within the corresponding performance time efficiency; through the time effect constraint, the satisfaction degree of the customer can be improved, and the customer experience is improved.
And (3) pin quantity fluctuation conduction constraint: the method is obtained by calculating the expansion factor, the sales fluctuation of the demand layer nodes, the single item flow and the total demand of the nodes capable of receiving the single item.
Specifically, the expansion factor is a penalty coefficient introduced in the sales fluctuation conduction constraint and is used for adjusting the number of the nodes. The expansion factor is related to the purchasing cost of the nodes of the purchasing layer, is a punishment coefficient artificially introduced, and realizes punishment on the number of the nodes by reducing the number of the nodes through the limitation on the purchasing cost. The sales fluctuation of the demand level node is linearly transmitted to the upper level inventory node (sequentially: middle level node and purchasing level node) based on the distribution of the single commodity flow, and the larger the single commodity flow is, the larger the sales fluctuation felt by the upper level inventory node is. Due to the fact that the inventory cost and the expansion factor of each node are different, sales volume fluctuation is imported into a warehouse with low inventory cost and small expansion factor, so that the minimum total purchase cost is guaranteed, the number of nodes is automatically reduced in the optimized scheme, and the purpose of meeting the requirements of local order satisfaction rate and timeliness through the minimum nodes is achieved.
And step S103, solving the objective function to realize the optimization of the inventory network.
Specifically, the solution result includes: optimizing the number of nodes and optimizing the number of inventory items for the nodes. By optimizing the number of the nodes, the problem of inventory network optimization such as warehouse network planning, site coverage optimization and the like is solved, and by optimizing the number of the inventory single products of the nodes, the problem of inventory network optimization such as inventory layout and the like is solved.
Specifically, according to the embodiment of the invention, a convex optimization technology is adopted, so that an objective function constructed based on an inventory network model is a convex function, and then an interior point method is adopted for solving. Through the arrangement, the convex optimization technology is adopted, and the solving speed can be obviously accelerated. However, it should be noted that the convex optimization technique is not used as a limitation of the present application, and the solving algorithm in the prior art, such as the heuristic algorithm, the branch-and-bound method, and other planning algorithms, can be applied to the solving of the objective function in the present invention.
Further, according to the embodiment of the present invention, if the solution result is a non-integer, the inventory network optimization method further includes: and carrying out rounding operation on the solved result so as to obtain the optimized inventory single product quantity with the numerical value as an integer.
Since the objective function constructed in the embodiment of the present invention mainly uses the single commodity flow as the optimization variable and is a continuity variable, when the optimization result may be a non-integer, a rounding operation (ceil function, rounding up) needs to be performed on the optimization result, so that the number of the single commodity flow on each performance support path and the number of the single commodities stored in the stock node are both integers. While the number of inventory nodes, another optimization result, also needs to satisfy the integer requirement.
According to the technical scheme provided by the embodiment of the invention, the inventory network model is constructed according to the node types in the inventory network and the performance relation among the nodes; constructing an objective function according to the constraint conditions and the inventory network model, wherein the constraint conditions comprise single item flow constraints; the technical means of solving the objective function to realize the optimization of the inventory network overcomes the technical problems of poor expandability and universality of an inventory network model, low solving speed of the objective function and poor optimization effect in the prior art, thereby greatly improving the universality and flexibility of the inventory network model and the solving speed of the objective function and remarkably improving the optimization effect of the inventory network.
FIG. 2a is a schematic diagram of a main flow of a singleton-based inventory network optimization method according to a second embodiment of the present invention; as shown in fig. 2a, the inventory network optimization method based on singleton flow according to the embodiment of the present invention includes:
step S201, classify the nodes in the inventory network, and obtain the number of various node types.
According to a specific implementation manner of the embodiment of the present invention, the nodes in the inventory network are divided into three categories:
purchasing a layer node: and the node is positioned at the upstream of the external supply network and the internal supply network of the inventory network, is used for purchasing various single products and can generate purchasing cost, and plays a role in supplying the single products to the middle layer and the demand layer. Such as a Central Distribution Center (CDC). The node of the purchasing layer has only one layer, and the number of the nodes of each layer can be multiple.
Intermediate layer nodes: and the node is positioned in the midstream of the inventory network and is used for receiving the single products supplied by the purchasing layer node and performing direct performance on a client or performing support relationship with a next-level inventory node (another intermediate layer node or a demand layer node), and only generates inventory cost and transportation and distribution cost without purchasing cost. Such as a Regional Distribution Center (RDC), a Front Distribution Center (FDC), etc. The nodes in the middle layer can be divided into a plurality of layers according to the level of fulfillment, and the number of the nodes in each layer can be multiple.
A demand layer node: and the node which is positioned at the downstream of the inventory network, receives the single products supplied by the middle layer node and directly faces the customer requirement, and mainly refers to a quantity Aggregated Place (SAP). The demand layer node is only one layer, but the number of nodes of the layer can be more.
Meanwhile, the number of various nodes is counted, and in a specific embodiment, the number of nodes of a purchasing layer is N _ p, the number of nodes of a middle layer is N _ inter, and the number of nodes of a demand layer is N _ sap.
Step S202, determining a fulfillment relationship matrix of the inventory network according to the node types and the fulfillment relationship among the nodes.
A fulfillment relationship matrix determined from the node types and the fulfillment relationships between the nodes is shown in fig. 2 b. The row node i represents a node (a purchasing layer node and a middle layer node) capable of supplying a single item in the inventory network, the column node j represents a node (a purchasing layer node, a middle layer node and a demand layer node) capable of receiving a single item in the inventory network, an element Aij in the fulfillment relation matrix Aij is 1, the node i can support the node j, namely, a fulfillment relation exists between the node i and the node j, the node i can supply a single item to the node j, or the node j cannot receive a single item supplied by the node i; aij being 0 indicates that node i may not support node j, i.e., there is no fulfillment relationship between node i and node j, and node i may not supply node j with the item. It should be noted that, in the inventory network, since a fulfillment relationship indirectly exists between any two nodes, the support relationship (fulfillment relationship) refers to a direct fulfillment relationship. It should be noted that the numerical values in fig. 2b are only one specific embodiment of the present invention, and do not represent specific limitations of the present invention.
And determining a fulfillment relation matrix of the inventory network according to the three node types and the fulfillment relation among the nodes. The method is beneficial to subsequently constructing a multi-layer fulfillment inventory network model, and solves the problem that the inventory network model in the prior art is only applicable to two-level network modeling and has poor universality.
And step S203, determining an aging matrix and a cost matrix of the inventory network according to the fulfillment relation matrix, and completing construction of an inventory network model. Wherein the inventory network model includes three matrices.
And determining an aging matrix and a cost matrix of the inventory network based on the allowable support relationship among various nodes. The structure of the time efficiency matrix and the cost matrix is the same as the performance relation matrix, and each element of the matrix is respectively represented as the performance time efficiency and the performance cost from the node i to the node j. The row node i represents the node where the SKU flows out and the column node j represents the node where the SKU flows in.
Specifically, if there is a direct support relationship (direct performance), that is, aij is 1, the corresponding positions of the aging matrix and the cost matrix are filled with the corresponding performance required aging and performance required cost, where the performance aging is the time required for completing one delivery from node i to node j; the performance cost refers to the delivery cost required to complete the delivery of an item from node i to node j. If there is no direct support relationship (indirect performing), i.e. aij is 0, a large number is filled in the corresponding positions of the aging matrix and the cost matrix (e.g. the corresponding position of the aging matrix is filled in for 9999 days, and the corresponding position of the cost matrix is filled in for 9999 yuan), where the purpose of filling the large number is only to indicate that there is no direct performing relationship between the two nodes, i.e. there is a disconnection between the two nodes in the inventory network. Similarly, the above meaning may be indicated by filling in english letters or mathematical symbols.
Through the steps, the constructed inventory network model can process a multilevel inventory fulfillment network and responsible network support relations, such as cross-level support, peer-level support and the like, and can calculate different cost items (purchase cost, delivery cost, inventory cost and the like) aiming at different node types.
And S204, constructing an objective function according to the constraint conditions and the inventory network model, wherein the constraint conditions comprise single item flow constraints.
And constructing an objective function on the basis of the constructed inventory network model so as to optimize the inventory network based on SKU flow distribution.
Specifically, the inventory network model includes:
a fulfillment relationship matrix: aij, N _ i N _ j
A cost matrix: cij, N _ i N _ j
An aging matrix: tij, N _ i N _ j
Wherein N _ i is N _ p + N _ inter; n _ j is N _ p + N _ inter + N _ sap.
The objective function was constructed as follows:
Figure BDA0002212997330000121
wherein: c [ i, j ]]For node i to node j delivery cost, SKU_flow[i，j]The number of the single products circulating from the node i to the node j; price [ i ]]Representing procurement of node iThen, the process is carried out; z is a radical ofαIndicating a service level (referring to a z-value corresponding to a mathematical confidence); u _ VLT [ i [ ]]Represents the mean of lead times (the time required for an item of supply represented here to travel from ordered to delivery to the target node); f [ i ]]Representing the transmission of the demand layer node to the node i; demand [ j ]]Representing the number of singlets required by the demand floor; p represents the capital occupation cost.
Based on the inventory network model, an objective function is constructed in combination with constraints, the objective function being constructed to minimize the sum of various costs, i.e., to minimize the sum of the total costs (the computable cost items mainly include delivery costs, procurement costs and inventory costs) in the entire inventory network.
The main constraints are as follows:
SKU flow constraint:
Figure BDA0002212997330000122
specifically, the single item flow is an optimization variable of the objective function, and the single item flow is restricted in the way that the number of the single items circulating between two nodes with a performing relation is larger than zero. An objective function constructed based on the inventory network model is optimized based on SKU flow, the distribution sizes of the SKU flow on different fulfillment support paths are different, when the SKU is zero, the fulfillment support path is indicated to be unavailable, namely, the fulfillment support relationship indicated by the path is not used during actual fulfillment; when the total sum of the total SKU flows flowing into a certain node is zero, the inventory node is indicated not to store the SKU, the starting condition of the inventory node can be judged through the distribution of the SKU flows, and then the optimization of the inventory node in the inventory network is realized.
And (3) requirement constraint:
Figure BDA0002212997330000123
meaning that the sum of the received singlets for the demand layer is equal to the total demand for the demand layer. The requirement constraint is met, the inventory cost of a demand layer is greatly reduced, the distribution cost of redundant single products is greatly reduced, and the optimization effect of the inventory layout is further improved.
Constraint on performing time efficiency:
Figure BDA0002212997330000131
Figure BDA0002212997330000132
wherein, T represents the time matrix of the stock network, beta represents the performance time requirement matrix, and is the matrix of N _ k × 2, wherein k represents the type of the performance time required to be satisfied. In the following beta matrix, the 0 th column of beta is the sales volume ratio required to meet the performance aging requirement, and the 1 st column is the sales volume ratio required to meet the corresponding performance aging (note, the expression method of network calculation is adopted here, and the first column actually represented by the matrix is called the 0 th column, and so on). For example, if the sales accounting ratio satisfied for the current day is not less than 0.25, the sales accounting ratio satisfied for the next day is not less than 0.46, the sales accounting ratio satisfied for the fourth day is not less than 0.70, and the beta matrix is expressed as:
Figure BDA0002212997330000133
according to the formula (4), when the performance aging from the node i to the node j in the aging matrix T in the inventory network model is larger than the aging required by the beta matrix at the k row and the 0 th column (note, the expression method of network calculation is adopted here, the first column actually represented by the matrix is called as the 0 th column, and so on), that the performance aging does not meet the constraint requirement, so the value is zero, and the performance path does not meet the requirement; otherwise, the value is 1 to meet the requirement.
And (5) judging whether the single item flow corresponding to the performance time efficiency from the node i to the node j in the time efficiency matrix T in the inventory network model meeting the k-class time efficiency requirements is larger than the actual sales demand of the node j. That is, for each type of performance aging requirement, it is required to achieve a certain sales volume ratio within the corresponding performance aging. Through the time effect constraint, the satisfaction degree of the customer can be improved, and the customer experience is improved.
And (3) pin quantity fluctuation conduction constraint:
Figure BDA0002212997330000141
the method is obtained by calculating an expansion factor gamma, the sales fluctuation F _ sap _ fluctuation [ j ] of the nodes of the demand layer, the single item flow SKU _ flow [ i, j ] and the total demand [ j ] of the nodes capable of receiving the single item. Wherein 1e-6 is a very small number expressed by scientific notation, namely 0.000001. The purpose is to prevent the denominator from being zero.
The expansion factor is a penalty coefficient introduced in the sales fluctuation transmission constraint and is used for adjusting the number of the nodes. The expansion factor is related to the purchasing cost of the nodes of the purchasing layer, is a punishment coefficient artificially introduced, and realizes punishment on the number of the nodes by reducing the number of the nodes through the limitation on the purchasing cost. The sales fluctuation of the demand level node is linearly transmitted to the upper level inventory node (sequentially: middle level node and purchasing level node) based on the distribution of the single commodity flow, and the larger the single commodity flow is, the larger the sales fluctuation felt by the upper level inventory node is. Due to the fact that the inventory cost and the expansion factor of each node are different, sales volume fluctuation is imported into a warehouse with low inventory cost and small expansion factor, so that the minimum total purchase cost is guaranteed, the number of nodes is automatically reduced in the optimized scheme, and the purpose of meeting the requirements of local order satisfaction rate and timeliness through the minimum nodes is achieved.
And S205, solving the objective function memorability to obtain the number of the optimized nodes and the number of the optimized inventory single products of the nodes so as to realize the optimization of the inventory network.
Further, according to the embodiment of the present invention, if the solution result is a non-integer, the inventory network optimization method further includes: and carrying out rounding operation on the solved result so as to obtain the optimized inventory single product quantity with the numerical value as an integer.
Since the objective function constructed in the embodiment of the present invention uses the singleton stream as the optimization variable and is a continuity variable, the optimization result may be a non-integer, and at this time, a rounding operation (ceil function, rounding up) needs to be performed on the optimization result, so that the number of the singleton streams on each performance support path and the number of the singletons stored in the inventory node are both integers.
According to the technical scheme provided by the embodiment of the invention, the inventory network model is constructed according to the node types in the inventory network and the performance relation among the nodes; constructing an objective function according to the constraint conditions and the inventory network model, wherein the constraint conditions comprise single item flow constraints; the objective function is solved to obtain an optimization result so as to realize the technical means of inventory network optimization, so that the technical problems of poor expandability and universality of an inventory network model, low solving speed of the objective function and poor optimization effect in the prior art are solved, the universality and flexibility of the inventory network model are greatly improved, and the technical effects of improving the solving speed and the optimization effect of the objective function are improved.
FIG. 3 is a schematic diagram of the main modules of a singles-flow based inventory network optimization apparatus, according to an embodiment of the present invention; as shown in fig. 3, an inventory network optimization device 300 based on singles flow according to an embodiment of the present invention includes:
the inventory network model building module 301 is configured to build an inventory network model according to the node types in the inventory network and the fulfillment relationship between the nodes.
The storage is taken as the inventory node in the inventory network, the storage is divided into different types according to the performance of the inventory node (inventory node), and then a multi-level inventory network model can be constructed according to actual needs according to the performance relation among different nodes.
Specifically, according to a specific implementation manner of the embodiment of the present invention, the node types include a purchasing layer node, an intermediate layer node and a demand layer node.
Purchasing a layer node: and the node is positioned at the upstream of the external supply network and the internal supply network of the inventory network, is used for purchasing various single products and can generate purchasing cost, and plays a role in supplying the single products to the middle layer and the demand layer. Such as a Central Distribution Center (CDC). The node of the purchasing layer has only one layer, and the number of the nodes of each layer can be multiple.
Intermediate layer nodes: and the node is positioned in the midstream of the inventory network and is used for receiving the single products supplied by the purchasing layer node and performing direct performance on a client or performing support relationship with a next-level inventory node (another intermediate layer node or a demand layer node), and only generates inventory cost and transportation and distribution cost without purchasing cost. Such as a Regional Distribution Center (RDC), a Front Distribution Center (FDC), etc. The nodes in the middle layer can be divided into a plurality of layers according to the level of fulfillment, and the number of the nodes in each layer can be multiple.
A demand layer node: and the node which is positioned at the downstream of the inventory network, receives the single products supplied by the middle layer node and directly faces the customer requirement, and mainly refers to a quantity Aggregated Place (SAP). The demand layer node is only one layer, but the number of nodes of the layer can be more.
Therefore, a multi-layer fulfillment inventory network model is constructed according to the three node types and the fulfillment relationship among the nodes, and the problem that the inventory network model in the prior art is poor in universality and only suitable for two-level network modeling is solved. It should be noted that, if the two-level inventory network model is adopted, the model only needs to be constructed according to the nodes of the purchase layer and the demand layer.
Further, according to the embodiment of the present invention, the inventory network model includes three matrices, and the inventory network model building module 301 is further configured to obtain a performance relationship matrix of the inventory network according to the types of nodes in the inventory network and the performance relationship between the nodes, and then determine the time-efficiency matrix and the cost matrix of the inventory network according to the performance relationship matrix, so as to complete the building of the inventory network model.
Specifically, the node numbers of the purchasing layer node, the intermediate layer node and the demand layer node are counted, and a fulfillment relationship matrix of the inventory network is obtained according to the fulfillment support relationship among the nodes, wherein in the fulfillment relationship matrix Aij,
a row node i represents a node (a purchasing layer node and a middle layer node) capable of supplying a single item in the inventory network, a column node j represents a node (a purchasing layer node, a middle layer node and a demand layer node) capable of receiving a single item in the inventory network, an element Aij in a fulfillment relation matrix Aij is 1, the node i can support the node j, namely, a fulfillment relation exists between the node i and the node j, and the node i can supply the single item to the node j or the node j cannot receive the single item supplied by the node i; aij being 0 indicates that node i may not support node j, i.e., there is no fulfillment relationship between node i and node j, and node i may not supply node j with the item. It should be noted that, in the inventory network, since a fulfillment relationship indirectly exists between any two nodes, the support relationship (fulfillment relationship) refers to a direct fulfillment relationship.
Further, after the fulfillment relationship matrix of the inventory network is obtained, the time-efficiency matrix and the cost matrix of the multi-layer fulfillment inventory network are determined based on the allowable support relationships (fulfillment relationship) among various nodes represented in the fulfillment relationship matrix. Specifically, if there is a direct support relationship (direct performance), that is, aij is 1, the corresponding positions of the aging matrix and the cost matrix are filled with the corresponding performance required aging and performance required cost, where the performance aging is the time required for completing one delivery from node i to node j; the performance cost refers to the delivery cost required to complete the delivery of an item from node i to node j. If there is no direct support relationship (indirect performing), i.e. aij is 0, a large number is filled in the corresponding positions of the aging matrix and the cost matrix (e.g. the corresponding position of the aging matrix is filled in for 9999 days, and the corresponding position of the cost matrix is filled in for 9999 yuan), where the purpose of filling the large number is only to indicate that there is no direct performing relationship between the two nodes, i.e. there is a disconnection between the two nodes in the inventory network. Similarly, the above meaning may be indicated by filling in english letters or mathematical symbols.
The multi-level fulfillment inventory network model constructed by the method can process a multi-level fulfillment inventory network and complex inventory network support relations, such as cross-level support, peer-level support and the like, can flexibly calculate different cost items (purchase cost, delivery cost, inventory cost and the like) aiming at different node types, is beneficial to reducing the difficulty of subsequently establishing an objective function according to cost constraint, further improves the solving speed and improves the optimization effect.
An objective function construction module 302 for constructing an objective function according to constraints and the inventory network model, wherein the constraints include singleton flow constraints.
Based on the inventory network model, an objective function is constructed in combination with constraints, the objective function being constructed so as to minimize the sum of various types of costs, i.e., to minimize the total cost in the entire inventory network. The single item flow is an optimization variable of the objective function, and the single item flow is restricted to be that the number of the single items circulating between two nodes with a performance relation is larger than zero. An objective function constructed based on the inventory network model is optimized based on SKU flow, the distribution sizes of the SKU flow on different fulfillment support paths are different, when the SKU is zero, the fulfillment support path is indicated to be unavailable, namely, the fulfillment support relationship indicated by the path is not used during actual fulfillment; when the total sum of the total SKU flows flowing into a certain node is zero, the inventory node is indicated not to store the SKU, the starting condition of the inventory node can be judged through the distribution of the SKU flows, and then the optimization of the inventory node in the inventory network is realized.
Specifically, the calculable costs mainly include: distribution costs, procurement costs, and inventory costs.
Distribution cost: in the whole inventory network, the total distribution cost of the single items circulating between the inventory nodes with direct fulfillment relationship exists, and the distribution cost between the two nodes with fulfillment relationship is calculated according to the fulfillment cost of the single item and the number of the fulfillment item flows;
and (3) purchasing cost: the cost generated by purchasing the single item on the purchasing layer is calculated according to the purchasing price, the service level, the mean value of the lead time and the sales fluctuation.
Inventory cost: the cost generated by storing the single products by the inventory nodes (the purchasing layer node, the middle layer node and the demand layer node) is calculated according to the service level, the average value of the lead time, the sales fluctuation and the capital occupation cost.
Further, the constraint condition further includes: demand constraints, aging constraints, and sales fluctuation conductance constraints.
And (3) requirement constraint: meaning that the sum of the received singlets for the demand layer is equal to the total demand for the demand layer. The requirement constraint is met, the inventory cost of a demand layer is greatly reduced, the distribution cost of redundant single products is greatly reduced, and the optimization effect of the inventory layout is further improved.
And (4) time efficiency constraint: the requirement of each type of performance time efficiency requires that a certain sales volume ratio is achieved within the corresponding performance time efficiency; through the time effect constraint, the satisfaction degree of the customer can be improved, and the customer experience is improved.
And (3) pin quantity fluctuation conduction constraint: the total demand of the nodes which can receive the single item is calculated by the sales fluctuation of the demand layer nodes, the expansion factor, the single item flow and the total demand of the nodes which can receive the single item.
Specifically, the expansion factor is a penalty coefficient introduced in the sales fluctuation conduction constraint and is used for adjusting the number of the nodes. The expansion factor is related to the purchasing cost of the nodes of the purchasing layer, is a punishment coefficient artificially introduced, and realizes punishment on the number of the nodes by reducing the number of the nodes through the limitation on the purchasing cost. The sales fluctuation of the demand level node is linearly transmitted to the upper level inventory node (sequentially: middle level node and purchasing level node) based on the distribution of the single commodity flow, and the larger the single commodity flow is, the larger the sales fluctuation felt by the upper level inventory node is. Due to the fact that the inventory cost and the expansion factor of each node are different, sales volume fluctuation is imported into a warehouse with low inventory cost and small expansion factor, so that the minimum total purchase cost is guaranteed, the number of nodes is automatically reduced in the optimized scheme, and the purpose of meeting the requirements of local order satisfaction rate and timeliness through the minimum nodes is achieved.
And the solving module 303 is configured to solve the objective function to achieve the inventory network optimization.
Specifically, the solution result includes: optimizing the number of nodes and optimizing the number of inventory items for the nodes. By optimizing the number of the nodes, the problem of inventory network optimization such as warehouse network planning, site coverage optimization and the like is solved, and by optimizing the number of the inventory single products of the nodes, the problem of inventory network optimization such as inventory layout and the like is solved.
Specifically, according to the embodiment of the invention, a convex optimization technology is adopted, so that an objective function constructed based on an inventory network model is a convex function, and then an interior point method is adopted for solving. Through the arrangement, the convex optimization technology is adopted, and the solving speed can be obviously accelerated. However, it should be noted that the convex optimization technique is not used as a limitation of the present application, and the solving algorithm in the prior art, such as the heuristic algorithm, the branch-and-bound method, and other planning algorithms, can be applied to the solving of the objective function in the present invention.
Further, according to the embodiment of the present invention, if the solution result is a non-integer, the inventory network optimization device 300 based on the single item flow further includes a rounding module, configured to round the solution result to obtain the optimized inventory single item quantity and the optimized node quantity with the numerical value as an integer.
Since the objective function constructed in the embodiment of the present invention uses the singleton stream as the optimization variable and is a continuity variable, the optimization result may be a non-integer, and at this time, a rounding operation (ceil function, rounding up) needs to be performed on the optimization result, so that the number of the singleton streams on each performance support path and the number of the singletons stored in the inventory node are both integers. While the number of inventory nodes, another optimization result, also needs to satisfy the integer requirement.
According to the technical scheme provided by the embodiment of the invention, the inventory network model is constructed according to the node types in the inventory network and the performance relation among the nodes; constructing an objective function according to the constraint conditions and the inventory network model, wherein the constraint conditions comprise single item flow constraints; the technical means of solving the objective function to realize the optimization of the inventory network overcomes the technical problems of poor expandability and universality of an inventory network model, low solving speed of the objective function and poor optimization effect in the prior art, thereby greatly improving the universality and flexibility of the inventory network model and simultaneously improving the technical effects of the solving speed and the optimization effect of the objective function.
FIG. 4 illustrates an exemplary system architecture 400 of a singles-flow based inventory network optimization method or singles-flow based inventory network optimization device to which embodiments of the invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405 (this architecture is merely an example, and the components included in a particular architecture may be adapted according to application specific circumstances). The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 401, 402, 403. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the inventory network optimization method based on singles flow provided by the embodiment of the present invention is generally executed by the server 405, and accordingly, the inventory network optimization device based on singles flow is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an inventory network model building module, an objective function building module, and a solving module. Where the names of these modules do not in some cases constitute a limitation of the module itself, for example, the objective function building module may also be described as a "module for building an objective function from constraints and an inventory network model".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: constructing an inventory network model according to the node types in the inventory network and the fulfillment relationship among the nodes; constructing an objective function according to the constraint conditions and the inventory network model, wherein the constraint conditions comprise single item flow constraints; and solving the objective function to realize the optimization of the inventory network.
According to the technical scheme of the embodiment of the invention, the inventory network model is constructed according to the node types in the inventory network and the performance relation among the nodes; constructing an objective function according to the constraint conditions and the inventory network model, wherein the constraint conditions comprise single item flow constraints; the technical means of solving the objective function to realize the optimization of the inventory network overcomes the technical problems of poor expandability and universality of an inventory network model, low solving speed of the objective function and poor optimization effect in the prior art, thereby greatly improving the universality and flexibility of the inventory network model and simultaneously improving the technical effects of the solving speed and the optimization effect of the objective function.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An inventory network optimization method based on single commodity flow is characterized by comprising the following steps:
constructing an inventory network model according to the node types in the inventory network and the fulfillment relationship among the nodes;
constructing an objective function according to a constraint condition and the inventory network model, wherein the constraint condition comprises a single item flow constraint;
and solving the objective function to realize the optimization of the inventory network.
2. The singleton-based inventory network optimization method of claim 1, wherein the inventory network model includes three matrices, and wherein the step of constructing the inventory network model includes: and obtaining a fulfillment relation matrix of the inventory network according to the node types in the inventory network and the fulfillment relation among the nodes, and determining a time-efficiency matrix and a cost matrix of the inventory network according to the fulfillment relation matrix.
3. The singleton-based inventory network optimization method of claim 2, wherein the node types include: the system comprises a purchasing layer node, a middle layer node and a demand layer node.
4. The singles-flow-based inventory network optimization method of claim 1, wherein the constraints further include: demand constraints, aging constraints, and sales fluctuation conductance constraints.
5. The singles-flow-based inventory network optimization method of claim 1, wherein solving the results comprises: optimizing the number of nodes and the optimized number of inventory items for the nodes.
6. The inventory network optimization method based on singles stream according to claim 5, wherein if the solution result is a non-integer, the inventory network optimization method further comprises: and carrying out rounding operation on the solving result.
7. An inventory network optimization device based on singles flow, comprising:
the inventory network model building module is used for building an inventory network model according to the node types in the inventory network and the performance relation among the nodes;
the target function building module is used for building a target function according to constraint conditions and the inventory network model, wherein the constraint conditions comprise single commodity flow constraints;
and the solving module is used for solving the objective function so as to realize the optimization of the inventory network.
8. The singles-flow based inventory network optimization device of claim 7, wherein the inventory network model includes three matrices, the inventory network model building module further configured to: obtaining a fulfillment relation matrix of the inventory network according to the node types in the inventory network and the fulfillment relation among the nodes, and determining a time-efficiency matrix and a cost matrix of the inventory network according to the fulfillment relation matrix.
9. A terminal, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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