CN113128763A - Enterprise supply chain demand optimization method based on dynamic planning - Google Patents
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Abstract
The invention relates to an enterprise supply chain demand optimization method based on dynamic planning, which comprises the following steps: first, generating a specific supply chain candidate chain set based on collaborative filtering: for any supply chain network G, if the weights of all edges are uniquely determined, after the supply chain network G is initialized, a specific supply chain candidate chain set is constructed by adopting a collaborative matrix decomposition method according to a given collaborative behavior mode; after the construction of the specific supply chain candidate chain set is completed, updating the intimacy of the supply chain in the specific supply chain candidate chain set according to the specific requirements of the enterprise, namely updating the weight of the edge in the supply chain network; and secondly, mining the collaborative behavior pattern based on the A-star algorithm.
Description
Technical Field
The invention belongs to the field of information recommendation optimization, and particularly relates to an enterprise supply chain demand optimization method based on dynamic planning.
Background
The supply requirements of enterprises change more and more frequently, the required supply chain form becomes more and more complex, with the wide application of the internet and big data, the enterprise supply chain networks are more widely interconnected, the dynamic and massive characteristics of resources are obvious, and the supply requirements of enterprise orders change, so that the construction of a supply chain meeting the specific supply requirements of the enterprises becomes difficult. How to select an optimal supply chain network according to the supply demand change of enterprises and accelerate and coordinate the dynamic supply chain of the required products and services of various enterprises based on the Internet is a problem to be solved urgently.
Drawings
The supply chain network of fig. 1 is an undirected graph with weights.
FIG. 2 shows the behavior patterns of suppliers, manufacturers, and parts.
FIG. 3 is a diagram of a collaborative pattern matching algorithm approach.
Disclosure of Invention
The invention aims to provide an enterprise supply chain demand optimization method based on dynamic planning. The technical scheme is as follows:
an enterprise supply chain demand optimization method based on dynamic planning comprises the following steps:
first, generation of a set of supply chain-specific candidates based on collaborative filtering
For any supply chain network G, if the weights of all edges are uniquely determined, after the supply chain network G is initialized, a specific supply chain candidate chain set is constructed by adopting a collaborative matrix decomposition method according to a given collaborative behavior mode, wherein the specific supply chain candidate chain set comprises brand business nodes nkConstructing a set of specific candidate chains, namely nkFor a node to be extended, the process is as follows: is a provider node nkConstructing a product candidate node set, and calculating each product candidate node and a brand name provider node nk(iii) the degree of intimacy therebetween; secondly, constructing a product part candidate node set for each sub-node in the product candidate node set by adopting the same method, and before constructing a specific supply chain candidate set, carrying out commodity candidate node n on a node to be expandedkCarrying out similarity screening;
after the construction of the candidate chain set of the specific supply chain is completed, updating the intimacy of the supply chain in the candidate chain set of the specific supply chain according to the specific requirements of the enterprise, namely updating the weight of the edge in the supply chain network, wherein the weight updating formula is
secondly, mining the collaborative behavior pattern based on A-star algorithm
According to a given collaborative behavior pattern, realizing collaborative behavior pattern matching based on an A-x algorithm, matching a supply chain set which accords with the collaborative behavior pattern in a specific supply chain candidate chain set, and screening a supply chain instance which accords with the specific requirements of an enterprise according to the specific requirements of the enterprise in the matched supply chain set;
further, the algorithm for constructing a candidate chain set of a specific supply chain is as follows:
inputting: supply chain network G, node n to be extendedk；
And (3) outputting: specific supply chain candidate set Lself；
Step 1, calculating a node n to be expanded in the same type of nodes by adopting a cosine similarity methodkSimilarity between the two groups is determined, and the similarity is sorted from big to small according to the intimacy;
step 2, calculating confidence intervals of the similarity of the supply chain nodes, taking the lower end points of the confidence intervals as similarity thresholds, and screening out the nodes n to be expandedkNode set P with similarity greater than similarity thresholdlist；
Step 3 adding PlistAdding subordinate node of nodes in set into subordinate node set ZlistPerforming the following steps;
step 4, constructing an intimacy matrix R, wherein the intimacy matrix R is equal to UTX V, U is the set of nodes PlistV is a set of nodes Zlist，RijRepresents PlistThe ith node in the set and ZlistAffinity between jth nodes in the set;
step 5, calculating an affinity matrix R by adopting a gradient descent-based collaborative matrix decomposition method;
step 6, calculating a K value by adopting a maximum likelihood estimation method;
step 7 in the affinity matrix R, nkNode A list of first K (in order of degree of intimacy) nodes as nkAnd added to a particular supply chain candidate set LselfPerforming the following steps;
step 8 if nkIf there is no subordinate node, that is, if the node is already expanded to the raw material supplier class node, ending the expansion of the specific supply chain candidate chain set, and going to step 9; otherwise, the node n to be extendedk←nkTurning to step 1 for subordinate nodes;
step 9 output specific supply chain candidate chain set Lself；
Further, collaborative behavior pattern matching is realized based on an A-x algorithm, and pattern matching is performed by utilizing the category information of the nodes in the searching process, wherein the method comprises the following steps:
inputting: specific supply chain candidate set LselfA cooperative behavior pattern T;
and (3) outputting: candidate set of supply chain results LR；
Step 1. supplier node n in cooperative behavior mode0Put into the supply chain selection set LsAnd n is0Marking as an initial node;
step 2 generates a list with an initial value of null, named as a candidate set L of supply chain resultsR；
Step 3 if provided to the supply chain selection set LsIf the matching result is empty, the supply chain which accords with the cooperative behavior mode cannot be matched, and the step 8 is switched;
step 4. selection of supply chain selection set LsThe heuristic function value (f) of(tn)＝g(tn)+h(tn)) The smallest node tn is put into the candidate chain set L of the supply chain resultsRWherein: g(tn)Representing the intimacy, h, of the initial node to the current node tn(tn)Representing an estimate of intimacy from tn to the raw material supplier class node;
step 5 if class(tn)E { raw materialSupplier class, and the raw material supplier class has all appeared, will be from n0Matching the supply chain from the node to the current node tn with the cooperative behavior pattern, and if the matching is successful, selecting a set L from the supply chain for the tn nodesIs removed and the set of subordinate nodes of tn is added to LsReturning to the step 3; otherwise, it will be from n0Supply chain of nodes to current node tn a candidate set of chains L from supply chain resultsRDeleting, and returning to the step 3;
step 6 if
The subordinate nodes of tn are put into the supply chain selection set and the step 3 is returned.
Step 7 if class(tn)E, e.g. the { part manufacturer class }, if the { part manufacturer class } does not completely appear, returning the father node of tn, and returning to the step 3;
step 8 outputs a candidate chain set L of supply chain resultsR；
Form a candidate set L of supply chain resultsRThen, the candidate chain set L of the supply chain result is selected through the clipping functionRThe supply chain example in (1) is cut, the last selected supply chain example is added into the supply chain network, and the supply chain cutting is to carry out the supply chain result candidate chain set L according to the principle of highest cost performance and the principle of maximum sum of affinityRThe supply chain instances in (1) are sorted by filtering, wherein a larger supply chain cut value sort indicates that the supply chain is more consistent with the enterprise-specific requirement, and a smaller sort value indicates that the supply chain is less consistent with the enterprise-specific requirement.
Detailed Description
The invention aims to provide a dynamic planning-based enterprise supply chain demand optimization method, which aims to solve the problem of how to match a specific supply chain example of an enterprise in a complex supply chain network by giving a cooperative behavior pattern, namely the problem of cooperative behavior pattern matching, and the basic idea is as shown in fig. 1: firstly, initializing a supply chain network, and constructing a cooperative behavior mode according to specific requirements of an enterprise; then constructing a specific supply chain candidate set according to a given cooperative behavior mode, and generating a supply chain result candidate set; and finally, screening out a supply chain instance meeting the specific requirements of the enterprise according to the specific requirements, and updating the supply chain instance into a supply chain network.
The problem to be solved by this patent is how to match an enterprise-specific supply chain set in a supply chain network according to a collaborative behavior pattern, and therefore the problem is described as a collaborative behavior pattern matching problem, i.e. matching a supply chain that conforms to a collaborative behavior pattern in a supply chain network.
Definitions 1 supply chain network G (V)d，Wd). The supply chain network studied by this patent is a weighted undirected graph G ═ Vd，Wd) As shown in fig. 1. Wherein: vdBeing a collection of vertices, the suppliers represented by the vertices belong to a particular class
Wherein, { C1，C2，C3，…，CnPossible values are suppliers, manufacturers, parts suppliers, etc.; wdIs a limited set of edges, which represents the intimacy between suppliers represented by the nodes, i.e. the closeness of the connection between suppliers, and can be obtained by calculating the parameters of credit between suppliers, order quantity, product quality, etc., and the supplier EiAnd EjThe intimacy degree between the two is calculated as
Affinityij＝Ei×Ej
A collaborative behavior pattern is defined 2. The cooperative behavior pattern refers to a sequence of related behaviors that different types of suppliers combine together for a certain purpose, and as shown in fig. 2, the cooperative behavior pattern must be composed of three types of suppliers, namely, a supplier, a manufacturer, and a component manufacturer. In fig. 2, the shaded nodes only mark the class of the node, and the unshaded nodes mark the specific provider. The process of selecting a supply chain instance in the supply chain network that conforms to the collaborative behavior pattern according to a given collaborative behavior pattern is referred to as collaborative behavior pattern matching.
Define 3 a supply chain instance. In a supply chain network, the path from a supplier node to a component producer node is referred to as a supply chain instance. The nodes of the manufacturer type are referred to as subordinate nodes of the supplier type nodes, and the nodes of the component manufacturer type are referred to as subordinate nodes of the manufacturer type nodes. The sum of the affinities of the various nodes that make up a supply chain instance is referred to as the affinity of that supply chain instance.
Supply chain node similarities are defined 4. Supply chain node similarity refers to the degree of similarity between nodes of the same type. The similarity between nodes can be calculated by adopting a cosine similarity method, and the cosine similarity formula is as follows:
in the formula, A and B respectively represent different nodes of the same type, the larger the cosine of the included angle is, the more similar the two nodes are, and the smaller the cosine of the included angle is, the lower the similarity of the two nodes is.
Define 5 supply chain clipping. The supply chain cutting refers to a process of sorting and screening matched supply chain examples through a cutting formula according to specific requirements of enterprises. The cutting formula is as follows:
sort＝wsum+APsum
in the formula WsumIndicates the intimacy of the supply chain, APsumRepresenting the cost/performance ratio of the supply chain, sort is referred to as the cut value of the supply chain.
The basic idea of the patent model is as follows: initializing a supply chain network, representing a supplier by each node in the supply chain network, and representing intimacy among the suppliers by the weight of edges among the nodes; constructing a corresponding cooperative behavior mode according to specific requirements of an enterprise; extending a specific candidate chain set from the supply chain network according to the cooperative behavior mode; searching and matching a proper supply chain candidate set in the supply chain network according to the cooperative behavior pattern; according to specific requirements, the method comprises the following steps: time, cost, etc. form the final supply chain solution and are updated into the supply chain network.
The cooperative behavior pattern matching algorithm is shown in FIG. 3
1. Collaborative filtering based generation of a particular supply chain candidate set
For any supply chain network G, if the weights of all edges are uniquely determined, after the supply chain network G is initialized, a specific supply chain candidate chain set is constructed by adopting a collaborative matrix decomposition method according to a given collaborative behavior mode. Mainly comprising a brand business node nkConstruction of a specific candidate set (n)kReferred to as a node to be extended), the process is as follows: is a provider node nkConstructing a product candidate node set, and calculating each product candidate node and a brand name provider node nk(iii) the degree of intimacy therebetween; secondly, constructing a product part candidate node set for each sub-node in the product candidate node set by adopting the same method. In order to reduce the complexity of the collaborative matrix decomposition algorithm, before a specific supply chain candidate set is constructed, a node commodity candidate node n to be expanded needs to be subjected to expansionkSimilarity screening is carried out, and an algorithm for constructing a specific supply chain candidate chain set is described as follows:
inputting: supply chain network G, node n to be extendedk。
And (3) outputting: specific supply chain candidate set Lself
Step 1, calculating a node n to be expanded in the same type of nodes by adopting a cosine similarity methodkSimilarity between them, and ordering from big to small according to intimacy.
Step 2, calculating confidence intervals of the similarity of the supply chain nodes, taking the lower end points of the confidence intervals as similarity thresholds, and screening out the nodes n to be expandedkNode set P with similarity greater than similarity thresholdlist
Step 3 adding PlistAdding subordinate node of nodes in set into subordinate node set zlistIn
Step 4, constructing an intimacy matrix R, wherein the intimacy matrix R is equal to UTX V, U is the set of nodes PlistV is the set of nodes zlist，RijRepresents PlistIth node in the set and zlistAffinity between jth nodes in the set.
Step 5, calculating the intimacy matrix R by adopting a cooperative matrix decomposition method based on gradient descent
Step 6, calculating the K value by adopting a maximum likelihood estimation method
Step 7 in the affinity matrix R, nkNode A list of first K (in order of degree of intimacy) nodes as nkAnd added to a particular supply chain candidate set LselfIn (1).
Step 8 if nkIf there is no subordinate node, that is, if the node is already expanded to the raw material supplier class node, ending the expansion of the specific supply chain candidate chain set, and going to step 9; otherwise, the node n to be extendedk←nkTurning to step 1.
Step 9 output specific supply chain candidate chain set Lself
And after the construction of the specific supply chain candidate chain set is completed, updating the intimacy of the supply chain in the specific supply chain candidate chain set according to the specific requirement of the enterprise, namely updating the weight of the edge in the supply chain network. The weight update formula is
2. Collaborative behavior pattern mining based on A-x algorithm
And after updating the specific supply chain candidate chain set, matching the supply chain set which accords with the cooperative behavior pattern in the specific supply chain candidate chain set according to the given cooperative behavior pattern, and screening a supply chain instance which accords with the specific requirement of the enterprise in the matched supply chain set according to the specific requirement of the enterprise. The patent realizes cooperative behavior pattern matching based on A-star algorithm, and performs pattern matching by using the category information of nodes in the search, wherein the description of the cooperative behavior pattern matching algorithm is as follows:
inputting: specific supply chain candidate set LselfThe cooperative behavior pattern T.
And (3) outputting: candidate set of supply chain results LR。
Step 1. supplier node n in cooperative behavior mode0Put into the supply chain selection set LsAnd n is0Marked as the initial node.
Step 2 generates a list with an initial value of null, named as a candidate set L of supply chain resultsR。
Step 3 if provided to the supply chain selection set LsIf it is empty, the supply chain conforming to the cooperative behavior pattern cannot be matched, and step 8 is performed.
Step 4. selection of supply chain selection set LsThe heuristic function value (f) of(tn)＝g(tn)+h(tn)) The smallest node tn is put into the candidate chain set L of the supply chain resultsRWherein: g(tn)Representing the intimacy, h, of the initial node to the current node tn(tn)Representing estimates of intimacy from tn to the raw material supplier class node.
Step 5 if class(tn)E.g. { raw material supplier class }, and the { raw material supplier class } has all appeared, will be from n0Matching the supply chain from the node to the current node tn with the cooperative behavior pattern, and if the matching is successful, selecting a set L from the supply chain for the tn nodesIs removed and the set of subordinate nodes of tn is added to LsReturning to the step 3; otherwise, it will be from n0Supply chain of nodes to current node tn a candidate set of chains L from supply chain resultsRAnd (5) deleting and returning to the step (3).
Step 6 if
The subordinate nodes of tn are put into the supply chain selection set and the step 3 is returned.
Step 7 if class(tn)E { part manufacturer class }, and if all of the { part manufacturer class } are not present, then returntn, returning to step 3.
Step 8 outputs a candidate chain set L of supply chain resultsR
Form a candidate set L of supply chain resultsRThen, the candidate chain set L of the supply chain result is selected through the clipping functionRThe supply chain instance in (1) is tailored and the last selected supply chain instance is added to the supply chain network. The supply chain cutting is to select a chain set L for the supply chain result candidate according to the principle of highest cost performance and the principle of maximum sum of affinityRThe supply chain instances in (1) are sorted by filtering, wherein a larger supply chain cut value sort indicates that the supply chain is more consistent with the enterprise-specific requirement, and a smaller sort value indicates that the supply chain is less consistent with the enterprise-specific requirement.
The invention relates to a method for optimizing enterprise supply chain requirements based on dynamic planning, which aims at the problem of supplier selection in an open and dynamic environment, and is a method for recommending suppliers to specific supply requirements of enterprises by utilizing the supply chain interactive relationship on the basis of the interactive relationship among various suppliers, manufacturers and part manufacturers in a supply chain on the basis of a supplier cooperative network so as to guide the selection of the suppliers, reduce the dependence on manual experience and improve the selection efficiency of the suppliers.
Various other modifications and changes may be made by those skilled in the art based on the above-described technical solutions and concepts, and all such modifications and changes should fall within the scope of the claims of the present invention.
Claims (3)
1. An enterprise supply chain demand optimization method based on dynamic planning comprises the following steps:
first, generation of a set of supply chain-specific candidates based on collaborative filtering
For any supply chain network G, if the weights of all edges are uniquely determined, after the supply chain network G is initialized, a specific supply chain candidate chain set is constructed by adopting a collaborative matrix decomposition method according to a given collaborative behavior mode, wherein the specific supply chain candidate chain set comprises brand business nodes nkConstructing a set of specific candidate chains, namely nkFor a node to be extended, the process is as follows: is supplyQuotient node nkConstructing a product candidate node set, and calculating each product candidate node and a brand name provider node nk(iii) the degree of intimacy therebetween; secondly, constructing a product part candidate node set for each sub-node in the product candidate node set by adopting the same method, and before constructing a specific supply chain candidate set, carrying out commodity candidate node n on a node to be expandedkAnd (5) carrying out similarity screening.
After the construction of the candidate chain set of the specific supply chain is completed, updating the intimacy of the supply chain in the candidate chain set of the specific supply chain according to the specific requirements of the enterprise, namely updating the weight of the edge in the supply chain network, wherein the weight updating formula is
secondly, mining the collaborative behavior pattern based on A-star algorithm
And according to a given cooperative behavior pattern, realizing cooperative behavior pattern matching based on an A-algorithm, matching a supply chain set which accords with the cooperative behavior pattern in a specific supply chain candidate chain set, and screening a supply chain instance which accords with the specific requirements of the enterprise according to the specific requirements of the enterprise in the matched supply chain set.
2. The method of claim 1, wherein the algorithm for constructing a particular supply chain candidate set is as follows:
inputting: supply chain network G, node n to be extendedk；
And (3) outputting: specific supply chain candidate set Lself；
Step 1, calculating identity by adopting a cosine similarity methodClass node n with node to be extendedkSimilarity between the two groups is determined, and the similarity is sorted from big to small according to the intimacy;
step 2, calculating confidence intervals of the similarity of the supply chain nodes, taking the lower end points of the confidence intervals as similarity thresholds, and screening out the nodes n to be expandedkNode set P with similarity greater than similarity thresholdlist；
Step 3 adding PlistAdding subordinate node of nodes in set into subordinate node set ZlistPerforming the following steps;
step 4, constructing an intimacy matrix R, wherein the intimacy matrix R is equal to UTX V, U is the set of nodes PlistV is a set of nodes Zlist，RijRepresents PlistThe ith node in the set and ZlistAffinity between jth nodes in the set;
step 5, calculating an affinity matrix R by adopting a gradient descent-based collaborative matrix decomposition method;
step 6, calculating a K value by adopting a maximum likelihood estimation method;
step 7 in the affinity matrix R, nkNode A list of first K (in order of degree of intimacy) nodes as nkAnd added to a particular supply chain candidate set LselfPerforming the following steps;
step 8 if nkIf there is no subordinate node, that is, if the node is already expanded to the raw material supplier class node, ending the expansion of the specific supply chain candidate chain set, and going to step 9; otherwise, the node n to be extendedk←nkTurning to step 1 for subordinate nodes;
step 9 output specific supply chain candidate chain set Lself。
3. The method according to claim 2, characterized in that a collaborative behavior pattern matching is achieved based on the a-x algorithm, and pattern matching is performed in a search using class information of nodes, the method comprising:
inputting: specific supply chain candidate set LselfA cooperative behavior pattern T;
and (3) outputting: candidate set of supply chain results LR；
Step 1. supplier node n in cooperative behavior mode0Put into the supply chain selection set LsAnd n is0Marking as an initial node;
step 2 generates a list with an initial value of null, named as a candidate set L of supply chain resultsR；
Step 3 if provided to the supply chain selection set LsIf the matching result is empty, the supply chain which accords with the cooperative behavior mode cannot be matched, and the step 8 is switched;
step 4. selection of supply chain selection set LsThe heuristic function value (f) of(tn)＝g(tn)+h(tn)) The smallest node tn is put into the candidate chain set L of the supply chain resultsRWherein: g(tn)Representing the intimacy, h, of the initial node to the current node tn(tn)Representing an estimate of intimacy from tn to the raw material supplier class node;
step 5 if class(tn)E.g. { raw material supplier class }, and the { raw material supplier class } has all appeared, will be from n0Matching the supply chain from the node to the current node tn with the cooperative behavior pattern, and if the matching is successful, selecting a set L from the supply chain for the tn nodesIs removed and the set of subordinate nodes of tn is added to LsReturning to the step 3; otherwise, it will be from n0Supply chain of nodes to current node tn a candidate set of chains L from supply chain resultsRDeleting, and returning to the step 3;
step 6 if
The subordinate nodes of tn are put into the supply chain selection set and the step 3 is returned.
Step 7 if class(tn)E, e.g. the { part manufacturer class }, if the { part manufacturer class } does not completely appear, returning the father node of tn, and returning to the step 3;
step 8 outputs a candidate chain set L of supply chain resultsR；
Form a candidate set L of supply chain resultsRThen, a candidate chain set is selected for the supply chain result through a clipping functionLRThe supply chain example in (1) is cut, the last selected supply chain example is added into the supply chain network, and the supply chain cutting is to carry out the supply chain result candidate chain set L according to the principle of highest cost performance and the principle of maximum sum of affinityRThe supply chain instances in (1) are sorted by filtering, wherein a larger supply chain cut value sort indicates that the supply chain is more consistent with the enterprise-specific requirement, and a smaller sort value indicates that the supply chain is less consistent with the enterprise-specific requirement.
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