A method, device and storage medium for constructing a supply chain network

Abstract

This invention discloses a method, apparatus, device, and storage medium for constructing a supply chain network. The method includes: using an acquired initial supply chain network as the current supply chain network; determining a current update type and obtaining the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type; wherein the competition score is determined using fuzzy hierarchical analysis based on expert evaluation scores of enterprise nodes; performing a modeling operation corresponding to the current update type on the current supply chain network based on at least one competition score to obtain an updated current supply chain network; repeatedly executing the step of determining the current update type based on the updated current supply chain network until a preset termination condition is met, and then using the updated current supply chain network as the target supply chain network. This invention solves the problem of inaccurate construction results in existing supply chain networks.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of supply chain technology, and in particular to a method, apparatus, equipment and storage medium for constructing a supply chain network. Background Technology

[0002] A supply chain network refers to a functional network structure formed by the connections between upstream and downstream members of the raw material suppliers, manufacturers, distributors, retailers, and end consumers involved in the production and distribution of products.

[0003] Supply chain networks are a type of complex network, a product of competition and cooperation among firms. In a supply chain network, each firm is a node, and the cooperative relationships between firms form the connections between nodes. In modern business operations, supply chain network management has a significant impact on corporate performance.

[0004] Existing supply chain network modeling methods only focus on supplier selection, neglecting other influencing factors or setting overly randomized conditions, resulting in the constructed supply chain network failing to accurately simulate the actual supply chain network. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for constructing a supply chain network, thereby improving the accuracy and practicality of the supply chain network and providing effective data support for subsequent enterprise management.

[0006] In a first aspect, embodiments of the present invention provide a method for constructing a supply chain network, the method comprising:

[0007] The obtained initial supply chain network is used as the current supply chain network; wherein, the initial supply chain network contains at least one enterprise node;

[0008] The current update type is determined, and the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type is obtained; wherein the competition score is determined by fuzzy hierarchical analysis based on the expert evaluation scores of enterprise nodes;

[0009] Based on at least one competition score, perform a modeling operation corresponding to the current update type on the current supply chain network to obtain an updated current supply chain network;

[0010] The steps to determine the current update type are repeated based on the updated current supply chain network until a preset termination condition is met, at which point the updated current supply chain network is taken as the target supply chain network.

[0011] Secondly, embodiments of the present invention also provide a supply chain network construction apparatus, the apparatus comprising:

[0012] An initial supply chain network acquisition module is used to acquire an initial supply chain network as the current supply chain network; wherein, the initial supply chain network contains at least one enterprise node;

[0013] The competition score acquisition module is used to determine the current update type and acquire the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type; wherein the competition score is determined by using fuzzy hierarchical analysis based on the expert evaluation scores of the enterprise nodes;

[0014] The current supply chain network update module is used to perform a modeling operation corresponding to the current update type on the current supply chain network based on at least one competition score, so as to obtain an updated current supply chain network;

[0015] The target supply chain network determination module is used to repeatedly execute the step of determining the current update type based on the updated current supply chain network until a preset termination condition is met, at which point the updated current supply chain network is taken as the target supply chain network.

[0016] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0017] One or more processors;

[0018] Memory, used to store one or more programs;

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-described methods for constructing the supply chain network.

[0020] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform any of the above-described methods for constructing a supply chain network.

[0021] This invention employs fuzzy hierarchical analysis (AHP) to determine the competitive scores of enterprise nodes based on expert evaluation scores. During the construction of the current supply chain network, it obtains the competitive scores of at least one enterprise node in the current supply chain network corresponding to the current update type. Based on at least one competitive score, it performs modeling operations corresponding to the current update type on the current supply chain network. This solves the problem of randomization in the construction conditions of existing supply chain networks. By applying the competitive relationship factors between enterprises in the supply chain network to the construction process, it improves the accuracy and practicality of the constructed target supply chain network, thus providing effective data support for subsequent enterprise management. Attached Figure Description

[0022] Figure 1 This is a flowchart of a method for constructing a supply chain network according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for constructing a supply chain network according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of a current supply chain network structure provided in Embodiment 2 of the present invention;

[0025] Figure 4 This is a schematic diagram of a supply chain network construction device provided in Embodiment 3 of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0027] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0028] Example 1

[0029] Figure 1 This is a flowchart of a method for constructing a supply chain network according to Embodiment 1 of the present invention. This embodiment is applicable to the modeling of supply chain networks. The method can be executed by a supply chain network construction device, which can be implemented in software and / or hardware. The device can be configured in a terminal device, such as a laptop computer, desktop computer, or other smart terminal. Specifically, it includes the following steps:

[0030] S110. Use the obtained initial supply chain network as the current supply chain network.

[0031] Supply chain networks, a type of complex network, consist of various business entities such as suppliers, manufacturers, distributors, retailers, and transporters, and are a product of competition and cooperation among enterprises. Supply chain networks are formed by the competitive and cooperative relationships between enterprises and possess characteristics such as adaptability, small-world nature, and scale-freeness. Specifically, the supply chain network in this embodiment of the invention can be constructed based on the Barabács network model. The Barabács network model, proposed by Barabács and Albert, is a scale-free network model that attributes the scale-free characteristics of real-world complex networks to growth mechanisms and priority connection mechanisms. The growth mechanism refers to the continuous expansion of the network size, while the priority connection mechanism means that new nodes tend to connect with nodes of higher degree, where degree represents the number of edges connecting a node.

[0032] Specifically, the initial supply chain network can be used to characterize the existing supply chain network before the construction of the supply chain network. In this embodiment, the initial supply chain network includes at least one enterprise node. For example, the types of enterprise nodes in the initial supply chain network include, but are not limited to, suppliers, manufacturers, distributors, retailers, or transporters. Of course, enterprise types are not limited to these; for example, supplier types can be further categorized into raw material suppliers and outsourced personnel suppliers, or into partner suppliers, priority suppliers, key suppliers, and commercial suppliers, etc. The enterprise types corresponding to the node networks included in the current supply chain network and the classification dimensions of each enterprise type are not limited here and can be arbitrarily set according to actual supply chain needs.

[0033] S120. Determine the current update type and obtain the competition score of at least one enterprise node in the current supply chain network that corresponds to the current update type.

[0034] In one embodiment, optionally, determining the current update type includes: generating a first random value at each time step, and determining whether the first random value is less than a preset growth threshold; if yes, the current update type is an addition type; if no, the current update type is an exit type.

[0035] Specifically, the time step can be used to characterize the interval between each update of the current supply chain network. For example, the time step could be 10 minutes, but the specific parameter value for the time step is not limited here.

[0036] Specifically, the preset growth threshold can be used to characterize the probability that the current update type is either a new type or an exit type. For example, the preset growth threshold can be 0.8, and the corresponding first random value can range from 0 to 1. For example, the preset growth threshold can be 80, and the corresponding first random value can range from 0 to 100. When the preset growth threshold is 0.8, the probability that the current update type is a new type is 0.8, and the probability that the current update type is an exit type is 0.2.

[0037] In one embodiment, optionally, obtaining the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type includes: generating a second random value, determining the enterprise type corresponding to the second random value, and obtaining the competition score of at least one enterprise node in the current supply chain network corresponding to the enterprise type based on the current update type.

[0038] Specifically, the current supply chain network includes at least one enterprise node corresponding to at least two enterprise types, with each enterprise type corresponding to a random value. For example, the random values ​​for suppliers, manufacturers, distributors, retailers, and transporters in the current supply chain network are 1, 2, 3, 4, and 5, respectively. Correspondingly, the range of the second random value is {1, 2, 3, 4, 5}.

[0039] In one embodiment, if the current update type is a new type, then based on the enterprise association list, the associated enterprise types that are related to the enterprise type are determined, and the competition score of at least one enterprise node in the current supply chain network corresponding to the associated enterprise type is obtained. The enterprise association list contains the associations between various enterprise types; for example, suppliers are associated with manufacturers and transporters, and distributors are associated with retailers, etc. Accordingly, when the enterprise type is a supplier, the competition score of the enterprise node in the current supply chain network corresponding to manufacturers and transporters is obtained; when the enterprise type is a distributor, the competition score of the enterprise node in the current supply chain network corresponding to retailers is obtained.

[0040] In another embodiment, if the current update type is an exit type, the competition score of the enterprise node corresponding to the enterprise type in the current supply chain network is obtained. For example, if the enterprise type is a supplier, the competition score of the enterprise node corresponding to the supplier in the current supply chain network is obtained.

[0041] In this embodiment, the competition score is determined using the fuzzy analytic hierarchy process (AHP) based on expert evaluation scores from enterprise nodes. Fuzzy AHP, proposed in the 1970s by Professor L.S. Thaaty of the Operations Research Institute in the United States, is a system analysis method that combines qualitative and quantitative approaches. Fuzzy AHP improves upon the problems of traditional analytic hierarchy process (AHP) and enhances the reliability of decision-making.

[0042] In one embodiment, optionally, the method further includes: for each enterprise node in the current supply chain network, obtaining an expert evaluation score and a priority relationship matrix group corresponding to the enterprise node; wherein, the priority relationship matrix group includes priority relationship matrices corresponding to at least two levels, the levels including an expert evaluation level and at least one indicator level, and the priority relationship matrix is ​​used to characterize the relative importance between pairs of elements in the current level; based on fuzzy hierarchical transformation rules, converting each priority relationship matrix in the priority relationship matrix group into a fuzzy consistent matrix, and determining the score weight based on each fuzzy consistent matrix; and determining the competition score corresponding to the enterprise node based on the expert evaluation score and the score weight.

[0043] Specifically, the hierarchy in the fuzzy hierarchical analysis method includes a target layer, at least one indicator layer, and an expert evaluation layer. For example, the target layer can be a selected target enterprise. The indicator layer includes a first-level indicator layer, a second-level indicator layer corresponding to the first-level indicator layer, a third-level indicator layer corresponding to the second-level indicator layer, and so on. Taking a fuzzy hierarchical analysis method with two indicator layers as an example, the first-level indicator layer contains two first-level indicator elements: cost and quality. The second-level indicator layer contains second-level indicator elements corresponding to the two first-level indicator elements. For example, second-level indicator elements include production cost, transportation cost, and transaction cost corresponding to cost, and pass rate, development quality, and customer complaint rate corresponding to quality, etc. Specifically, the expert evaluation layer includes at least one expert corresponding to each indicator element in the indicator layer. For example, the expert evaluation layer includes three experts corresponding to cost and four experts corresponding to product quality.

[0044] This embodiment uses one indicator layer for illustration. Assuming the indicator layer includes product price and product quality, the priority relationship matrix A corresponding to the indicator layer can be: The priority relationship matrix A can be used to characterize the relative importance of product quality and product price. For example, 0.26 indicates that product price is more important than product quality. In the example above, product quality is more important than product price.

[0045] Assuming the expert evaluation layer comprises 4 experts evaluating product price and 3 experts evaluating product quality, the priority relationship matrix corresponding to the expert evaluation layer includes priority relationship matrix B for the 4 experts and priority relationship matrix C for the 3 experts. For example, priority relationship matrix B is... Priority matrix B can be used to characterize the relative importance of the four experts evaluating product prices compared to each other; for example, 0.62 indicates that expert 4 is more important than expert 1. Priority matrix C is... The priority relationship matrix C can be used to characterize the relative importance of three experts evaluating product quality. For example, 0.62 indicates that expert 3 is more important than expert 1.

[0046] Specifically, the fuzzy level transformation rule satisfies the following formula:

[0047]

[0048]

[0049] Where, r i r represents the sum of the values ​​of the elements in the i-th row of the priority relation matrix. j r represents the sum of the values ​​of the elements in the j-th row of the priority relation matrix. ik Let r represent the value of the matrix element in the i-th row and k-th column of the priority relation matrix. ij This represents the value of the matrix element in the i-th row and j-th column of the fuzzy consistency matrix, where n represents the number of rows or columns in the priority relation matrix.

[0050] Specifically, the fuzzy consistency matrix A corresponding to the priority relation matrix A is: The fuzzy consistency matrix B corresponding to the priority relation matrix B is: The fuzzy consistency matrix C corresponding to the priority relation matrix C is:

[0051] Specifically, the score weights are determined based on each fuzzy consistency matrix, including: determining the element weights of each level relative to the previous level based on the fuzzy hierarchical weight formula and each fuzzy consistency matrix, and determining the score weights based on the element weights.

[0052] Specifically, in the fuzzy hierarchical analysis method, the levels of the target layer, first-level indicator layer, second-level indicator layer, third-level indicator layer, and expert evaluation layer decrease sequentially. Correspondingly, the element weights determined based on the fuzzy consistency matrix corresponding to the first-level indicator layer can be used to characterize the element weights of the first-level indicator layer relative to the target layer; the element weights determined based on the fuzzy consistency matrix corresponding to the second-level indicator layer can be used to characterize the element weights of the second-level indicator layer relative to the first-level indicator layer, and so on.

[0053] The formula for fuzzy hierarchical weights satisfies:

[0054]

[0055] Among them, w i This represents the weight corresponding to the i-th element, and n represents the number of rows or columns in the fuzzy consistency matrix. r ij This represents the value of the matrix element in the i-th row and j-th column of the fuzzy consistency matrix.

[0056] For example, take The element weights determined based on fuzzy consistent matrices A, B, and C are W, respectively. A =(0.301587,0.698413), W B =(0.23006,0.243234,0.256588,0.270117) and W C =(0.30662,0.326368,0.36696).

[0057] Specifically, the score weight W satisfies the formula:

[0058] W = (W A1 ×W B W A2 ×W C )

[0059] Among them, W A1 W A The weight of the first element, W A2 W A The weight of the second element in the middle.

[0060] The score weights are W = (0.0693, 0.0734, 0.0774, 0.00815, 0.2141, 0.2279, 0.2564).

[0061] Specifically, expert evaluation scores can be used to characterize the evaluation indicators given by experts to enterprise nodes. For example, assuming that the expert evaluation scores of the four experts for enterprise node A on product price are 90, 95, 85 and 90 respectively, and the expert evaluation scores of the three experts on product quality are 90, 80 and 90 respectively, then the competitive score of enterprise node A is 87.701.

[0062] S130. Based on at least one competition score, perform a modeling operation on the current supply chain network corresponding to the current update type to obtain the updated current supply chain network.

[0063] Specifically, when the current update type is "addition," based on the competition score of at least one enterprise node, the enterprise nodes to be connected corresponding to the new enterprise are determined. The new enterprise is added as a new enterprise node to the current supply chain network, and a connection relationship is established between the new enterprise node and at least one enterprise node to be connected, resulting in the updated current supply chain network. For example, the competition scores can be sorted in descending order, and a predetermined proportion of enterprise nodes in the sorting results can be selected as enterprise nodes to be connected. For instance, assuming there are 10 enterprise nodes and the predetermined proportion is 30%, the top 3 enterprise nodes in the sorting results can be selected as enterprise nodes to be connected.

[0064] When the current update type is exit type, based on the competition score of at least one enterprise node, the exiting enterprise node is determined and removed from the current supply chain network, resulting in an updated current supply chain network. For example, the competition scores can be sorted in descending order, and the enterprise node with the lowest competition score in the sorted results can be selected as the exiting enterprise node.

[0065] S140. Determine whether the preset termination condition is met. If yes, execute S150; otherwise, execute S120.

[0066] Specifically, the preset termination condition can be that the number of enterprise nodes in the updated current supply chain network reaches a preset node number threshold, and / or that the number of time steps reaches a preset step size threshold. For example, assuming the initial supply chain network has 10 enterprise nodes, the preset node number threshold can be 20, meaning the target supply chain network has 20 enterprise nodes. For example, the preset step size threshold can be 10, meaning that the target supply chain network is obtained by performing 10 update operations on the initial supply chain network.

[0067] S150. Use the updated current supply chain network as the target supply chain network.

[0068] The technical solution of this embodiment uses fuzzy hierarchical analysis to determine the competition score of enterprise nodes based on expert evaluation scores. During the construction of the current supply chain network, it obtains the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type. Based on at least one competition score, it performs modeling operations corresponding to the current update type on the current supply chain network. This solves the problem of randomization in the construction conditions of existing supply chain networks. By applying the competitive relationship factors between enterprises in the supply chain network to the construction process, it improves the accuracy and practicality of the constructed target supply chain network, thus providing effective data support for subsequent enterprise management.

[0069] Example 2

[0070] Figure 2 This is a flowchart of a method for constructing a supply chain network provided in Embodiment 2 of the present invention. The technical solution of this embodiment is a further refinement based on the above embodiments.

[0071] The specific implementation steps of this embodiment include:

[0072] S210. Use the obtained initial supply chain network as the current supply chain network.

[0073] S220. Generate the first random value within each time step.

[0074] S230. Determine whether the first random value is less than the preset growth threshold. If yes, execute S240; otherwise, execute S280.

[0075] S240. Obtain the competition score of at least one enterprise node in the current supply chain network that corresponds to the new type.

[0076] In one embodiment, optionally, the current supply chain network includes at least two layers of node networks, each node network including at least one enterprise node, and obtaining the competition score of at least one enterprise node in the current supply chain network corresponding to the new type includes: generating a second random value, and determining a target node network based on the second random value and a preset probability range corresponding to each node network; and obtaining the competition score of at least one enterprise node corresponding to the target node network based on the new type.

[0077] Specifically, each layer of the node network corresponds to a type of enterprise. For example, the current supply chain network includes at least two of the following: supplier node network, manufacturer node network, distributor node network, retailer node network, and transporter node network. The supplier node network includes at least one supplier enterprise, and the manufacturer node network includes at least one manufacturer enterprise.

[0078] Figure 3 This is a schematic diagram of a current supply chain network structure provided in Embodiment 2 of the present invention. Specifically, Figure 3 The current supply chain network shown comprises three layers: Node Network A, Node Network B, and Node Network C. Node Network A contains six enterprise nodes, while Node Networks B and C each contain four enterprise nodes. In this current supply chain network, each enterprise node has at least one connected enterprise node. Enterprise nodes in this network are not limited to connections with nodes in the same or adjacent layers; they can also be connected to enterprise nodes in non-adjacent and non-same layers, such as… Figure 3The first enterprise node in node network A is connected to the second enterprise node in node network C, and the fifth enterprise node in node network A is connected to the third enterprise node in node network C. Specifically, the edges between enterprise nodes in each layer of the node network mostly represent competitive relationships, while the edges between enterprise nodes in the middle of the node network mostly represent cooperative relationships.

[0079] For example, assuming the current supply chain network includes node network 1, node network 2, node network 3, node network 4, node network 5, and node network 6, the preset probability ranges corresponding to each node network can be [0, 0.2], (0.2, 0.5], (0.5, 0.7], (0.7, 0.85], (0.85, 0.95], and (0.95, 1) respectively. When the second random value is 0.3, the target node network is node network 2.

[0080] Specifically, based on the node network association list, associated node networks that are related to the target node network are identified, and the competitive scores of enterprise nodes in the associated node networks within the current supply chain network are obtained. The node network association list contains the relationships between various node networks. For example, a supplier's node network is associated with both the manufacturer's and the transporter's node networks. Accordingly, when the target node network is the supplier's node network, the competitive scores of enterprise nodes in the manufacturer's and transporter's node networks are obtained.

[0081] The advantage of this setup is that it enables hierarchical management of the current supply chain network, improving its structure and making its content clearer and more intuitive, thus facilitating users in understanding the enterprise nodes and their connections within the supply chain network.

[0082] S250: Obtain the newly added enterprise and the corresponding preset score threshold, and add the newly added enterprise as a new enterprise node to the current supply chain network.

[0083] In one embodiment, optionally, a list of newly added enterprises is obtained; wherein the list of newly added enterprises includes at least one candidate enterprise and a competition score corresponding to each candidate enterprise; the competition scores corresponding to at least one candidate enterprise in the list of newly added enterprises are sorted, and the candidate enterprise with the highest competition score in the sorting result is selected as the newly added enterprise; and a preset score threshold corresponding to the newly added enterprise in the list of newly added enterprises is obtained.

[0084] The competition score is also calculated using fuzzy hierarchical analysis. The preset score threshold in the list of new enterprises can be pre-set by the user or determined based on the competition scores of the enterprise nodes corresponding to the new type in the current supply chain network. For example, the preset score threshold can be the median, average, or a preset proportion of the competition scores of the enterprise nodes corresponding to the new type in the current supply chain network. For instance, assuming the competition scores are ranked as 90, 87, 85, 83, and 82, and the preset proportion is 40%, then the preset score threshold is 87.

[0085] The advantage of this setup is that it allows the competitive factors among companies to be added to the current supply chain network to be applied to the construction process of the supply chain network, thereby further improving the accuracy and practicality of the resulting target supply chain network.

[0086] Specifically, new enterprises will be added as new enterprise nodes to the target node network in the current supply chain network.

[0087] S260. For each enterprise node corresponding to the newly added type, if the competition score of the enterprise node is greater than the preset score threshold, then the enterprise node will be used as an enterprise node to be connected.

[0088] For example, if the preset score threshold is 80 and the competition score of enterprise node A corresponding to the newly added type is 81, then enterprise node A will be selected as the enterprise node to be connected. Specifically, at least one enterprise node to be connected is obtained based on the preset score threshold.

[0089] S270. Perform connection operations on the new enterprise node and at least one enterprise node to be connected to obtain the updated current supply chain network, and then execute S291.

[0090] S280. Obtain the competition score of at least one enterprise node in the current supply chain network that corresponds to the exit type.

[0091] In one embodiment, optionally, obtaining the competition score of at least one enterprise node in the current supply chain network corresponding to the exit type includes: generating a second random value, and determining a target node network based on the second random value and a preset probability range corresponding to each node network; and obtaining the competition score of at least one enterprise node corresponding to the target node network based on the exit type.

[0092] Specifically, the preset probability range for the exit type and the preset probability range for the new type can be the same or different. Specifically, the competition scores of enterprise nodes in the target node network within the current supply chain network are obtained. For example, if the enterprise type corresponding to the target node network is a supplier, then the obtained competition scores of the enterprise nodes are all supplier competition scores.

[0093] S290. Sort the competition scores of at least one enterprise node corresponding to the exit type, and delete the enterprise node with the lowest competition score from the current supply chain network to obtain the updated current supply chain network.

[0094] For example, if the ranking results of the competition scores of at least one enterprise node corresponding to the exit type are 90, 87, 85, 83 and 82 respectively, then the enterprise node with a competition score of 82 is regarded as the exiting enterprise node and is deleted from the current supply chain network.

[0095] S291. Determine whether the preset termination condition is met. If yes, execute S292; otherwise, execute S220.

[0096] S292. Use the updated current supply chain network as the target supply chain network.

[0097] Based on the above embodiments, optionally, the method further includes: obtaining the number of enterprise nodes in the target supply chain network corresponding to at least two degree values ​​respectively, and determining the degree distribution map based on the number of at least two enterprise nodes.

[0098] The degree can be used to represent the number of edges corresponding to a business node. For example, for a series of degree values, such as {1, 2, 3, 4}, the number of business nodes corresponding to each degree value is obtained. Specifically, the horizontal axis of the degree distribution graph represents the degree value, and the vertical axis represents the probability of the number of nodes. The probability of the number of nodes satisfies the formula:

[0099]

[0100] Where p(k) represents the probability of the number of nodes corresponding to degree value k, n(k) represents the number of enterprise nodes corresponding to degree value k, and N represents the total number of enterprise nodes in the target supply chain network.

[0101] Based on the above embodiments, optionally, the method further includes: obtaining the number of enterprise nodes in the target supply chain network corresponding to at least two degree values ​​respectively, and determining the power law exponent based on the number of at least two enterprise nodes.

[0102] Specifically, based on the probability of each degree value, a power function is fitted to obtain the power law exponent. The power function fit satisfies p(k)~k -r , where r represents the power law exponent.

[0103] The advantage of this setup is that, by determining the degree distribution graph and the power-law exponent, the scale-free characteristics of the target supply chain network can be analyzed. The degree distribution graph corresponding to the target supply chain network is characterized by most nodes having very small degrees, a few nodes having very large degrees, and the degree distribution graph exhibiting a long-tailed distribution.

[0104] Based on the above embodiments, optionally, the method further includes: for each enterprise node in the target supply chain network, determining the sub-clustering coefficient corresponding to the enterprise node based on the degree of the enterprise node and the number of triangle structures corresponding to the enterprise node, and determining the clustering coefficient corresponding to the target supply chain network based on at least three sub-clustering coefficients.

[0105] Specifically, the clustering coefficient can be used to describe the degree to which vertices in a target supply chain network cluster together; that is, the degree of interconnectivity among enterprise nodes connected to a given enterprise node. The clustering coefficient can be the average of at least three sub-clustering coefficients.

[0106] In a target supply chain network, if two enterprise nodes connected to an enterprise node are also connected, it is called a triangular structure corresponding to the enterprise node. The sub-clustering coefficient C... i Satisfying the formula:

[0107]

[0108] Where, k i E represents the degree of the i-th enterprise node. i This represents the number of triangle structures corresponding to the i-th enterprise node.

[0109] Based on the above embodiments, optionally, the method further includes: obtaining the number of edges on the shortest path between any two nodes in the target supply chain network, and determining the average path length corresponding to the target supply chain network based on the number of at least two edges.

[0110] Specifically, the average path length can be used to describe the average number of edges between any two nodes. The average path length L satisfies the formula:

[0111]

[0112] Where N represents the number of enterprise nodes in the target supply chain network, d ij This represents the number of edges on the shortest path between the i-th enterprise node and the j-th enterprise node.

[0113] The advantage of this setup is that by determining the clustering coefficient and average path length of the target supply chain network, its small-world characteristics can be analyzed. The target supply chain network is characterized by a relatively short average path length and a high clustering coefficient.

[0114] The technical solution of this embodiment solves the problem of chaotic supply chain network structure by setting up a hierarchical structure for the current supply chain network, making the structure of the current supply chain network clearer and more intuitive, and facilitating users to sort out the enterprise nodes and their connections in the supply chain network. Furthermore, this embodiment addresses the problem of overly random supply chain network construction by applying competitive relationship factors between enterprises in the current supply chain network and those to be added to the current supply chain network to the supply chain network construction process, thereby improving the accuracy and practicality of the constructed target supply chain network.

[0115] Example 3

[0116] Figure 4 This is a schematic diagram of a supply chain network construction device provided in Embodiment 3 of the present invention. This embodiment is applicable to the modeling of supply chain networks. The device can be implemented in software and / or hardware and can be configured on a terminal device. The supply chain network construction device includes: an initial supply chain network acquisition module 310, a competition score acquisition module 320, a current supply chain network update module 330, and a target supply chain network determination module 340.

[0117] The initial supply chain network acquisition module 310 is used to acquire the initial supply chain network as the current supply chain network; wherein, the initial supply chain network contains at least one enterprise node;

[0118] The competition score acquisition module 320 is used to determine the current update type and acquire the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type; wherein, the competition score is determined by using fuzzy hierarchical analysis based on the expert evaluation scores of enterprise nodes;

[0119] The current supply chain network update module 330 is used to perform a modeling operation corresponding to the current update type on the current supply chain network based on at least one competition score, so as to obtain an updated current supply chain network.

[0120] The target supply chain network determination module 340 is used to repeatedly execute the step of determining the current update type based on the updated current supply chain network until a preset termination condition is met, and then use the updated current supply chain network as the target supply chain network.

[0121] The technical solution of this embodiment uses fuzzy hierarchical analysis to determine the competition score of enterprise nodes based on expert evaluation scores. During the construction of the current supply chain network, it obtains the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type. Based on at least one competition score, it performs modeling operations corresponding to the current update type on the current supply chain network. This solves the problem of randomization in the construction conditions of existing supply chain networks. By applying the competitive relationship factors between enterprises in the supply chain network to the construction process, it improves the accuracy and practicality of the constructed target supply chain network, thus providing effective data support for subsequent enterprise management.

[0122] Based on the above technical solution, optionally, the device further includes:

[0123] The priority relationship matrix group acquisition module is used to acquire the expert evaluation score and priority relationship matrix group corresponding to each enterprise node in the current supply chain network. The priority relationship matrix group includes priority relationship matrices corresponding to at least two levels, and the levels include an expert evaluation level and at least one indicator level. The priority relationship matrix is ​​used to characterize the relative importance between pairs of elements in the current level.

[0124] The score weight determination module is used to convert each priority relation matrix in the priority relation matrix group into a fuzzy consistent matrix based on the fuzzy hierarchical transformation rule, and to determine the score weight based on each fuzzy consistent matrix.

[0125] The competition score determination module is used to determine the competition score corresponding to the enterprise node based on expert evaluation scores and score weights.

[0126] Based on the above technical solution, optionally, the competition score acquisition module 320 includes:

[0127] The current update type determination unit is used to generate a first random value within each time step and determine whether the first random value is less than a preset growth threshold; if so, the current update type is the addition type; if not, the current update type is the exit type.

[0128] Based on the above technical solution, optionally, the current supply chain network update module 330 includes:

[0129] The newly added enterprise acquisition unit is used to acquire newly added enterprises and their corresponding preset score thresholds when the current update type is "new".

[0130] The "Add New Enterprise" unit is used to add new enterprises as new enterprise nodes to the current supply chain network.

[0131] The unit for determining enterprise nodes to be connected is used to determine if the competition score of each enterprise node corresponding to the new type is greater than a preset score threshold.

[0132] The first current supply chain network update unit is used to perform connection operations on the new enterprise node and at least one enterprise node to be connected, respectively, to obtain the updated current supply chain network.

[0133] Based on the above technical solution, optionally, an enterprise acquisition unit can be added, specifically for:

[0134] Retrieve the list of newly added companies; the list of newly added companies includes at least one candidate company and the competition score corresponding to each candidate company.

[0135] Sort the competition scores of at least one candidate company in the list of new companies, and select the candidate company with the highest competition score in the sorting results as the new company.

[0136] Retrieve the preset score threshold corresponding to the newly added enterprise from the list of newly added enterprises.

[0137] Based on the above technical solution, optionally, the current supply chain network update module 330 includes:

[0138] The competition score sorting unit is used to sort the competition scores of at least one enterprise node corresponding to the current update type when the current update type is exit type.

[0139] The second current supply chain network update unit is used to delete the enterprise node corresponding to the lowest competition score in the ranking results from the current supply chain network to obtain the updated current supply chain network.

[0140] Based on the above technical solution, optionally, the current supply chain network includes at least two layers of node networks, each containing at least one enterprise node. Correspondingly, the competition score acquisition module 320 is specifically used for:

[0141] A second random value is generated, and the target node network is determined based on the second random value and the preset probability range corresponding to each node network.

[0142] Based on the current update type, obtain the competition score of at least one enterprise node corresponding to the target node network.

[0143] The supply chain network construction apparatus provided in this embodiment of the invention can be used to execute the supply chain network construction method provided in this embodiment of the invention, and has the corresponding functions and beneficial effects of the execution method.

[0144] It is worth noting that in the embodiments of the above-mentioned supply chain network construction device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0145] Example 4

[0146] Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The present invention provides services for the implementation of the supply chain network construction method of the above embodiments of the present invention, and can configure the supply chain network construction device in the above embodiments. Figure 5 A block diagram is shown of an exemplary electronic device 12 suitable for implementing embodiments of the present invention. Figure 5 The electronic device 12 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0147] like Figure 5 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0148] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0149] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0150] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0151] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0152] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the electronic device 12, and / or with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 5 As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0153] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the supply chain network construction method provided in the embodiments of the present invention.

[0154] The aforementioned electronic devices solve the problem of randomized construction conditions in existing supply chain networks. They apply the competitive relationship factors among enterprises in the supply chain network to the construction process, improving the accuracy and practicality of the constructed target supply chain network and providing effective data support for subsequent enterprise management.

[0155] Example 5

[0156] Embodiment 5 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a method for constructing a supply chain network, the method comprising:

[0157] The obtained initial supply chain network is used as the current supply chain network; wherein, the initial supply chain network contains at least one enterprise node;

[0158] The current update type is determined, and the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type is obtained; wherein, the competition score is determined by fuzzy hierarchical analysis based on the expert evaluation scores of enterprise nodes;

[0159] Based on at least one competition score, perform modeling operations corresponding to the current update type on the current supply chain network to obtain the updated current supply chain network;

[0160] The steps to determine the current update type are repeated based on the updated current supply chain network until a preset termination condition is met, at which point the updated current supply chain network is taken as the target supply chain network.

[0161] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0162] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0163] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0164] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0165] Of course, the computer-executable instructions provided in the embodiments of the present invention are not limited to the above-described method operations, but can also execute related operations in the supply chain network construction method provided in any embodiment of the present invention.

[0166] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for constructing a supply chain network, characterized in that, include: The obtained initial supply chain network is used as the current supply chain network; wherein, the initial supply chain network contains at least one enterprise node; The current update type is determined, and the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type is obtained; wherein the competition score is determined by fuzzy hierarchical analysis based on the expert evaluation scores of enterprise nodes; Based on at least one competition score, perform a modeling operation corresponding to the current update type on the current supply chain network to obtain an updated current supply chain network; Based on the updated current supply chain network, the steps to determine the current update type are repeated until a preset termination condition is met, at which point the updated current supply chain network is taken as the target supply chain network. The method further includes: For each enterprise node in the current supply chain network, obtain the expert evaluation score and priority relationship matrix group corresponding to the enterprise node; wherein, the priority relationship matrix group includes priority relationship matrices corresponding to at least two levels respectively, the levels include an expert evaluation level and at least one indicator level, and the priority relationship matrix is ​​used to characterize the relative importance between pairs of elements in the current level; Based on the fuzzy hierarchical transformation rule, each priority relation matrix in the priority relation matrix group is converted into a fuzzy consistent matrix. Based on the fuzzy hierarchical weight formula and each fuzzy consistent matrix, the element weights of each level relative to the previous level are determined. Based on the element weights, the score weights are determined. Based on the expert evaluation scores and the score weights, the competition score corresponding to the enterprise node is determined.

2. The method according to claim 1, characterized in that, Determining the current update type includes: Within each time step, a first random value is generated, and it is determined whether the first random value is less than a preset growth threshold. If so, the current update type is a new type; If not, then the current update type is an exit type.

3. The method according to claim 2, characterized in that, The step of performing a modeling operation corresponding to the current update type on the current supply chain network based on at least one competition score to obtain an updated current supply chain network includes: When the current update type is a new type, obtain the new enterprise and the preset score threshold corresponding to the new enterprise; The newly added enterprise is added as a new enterprise node to the current supply chain network; For each enterprise node corresponding to the newly added type, if the competition score of the enterprise node is greater than the preset score threshold, then the enterprise node is designated as an enterprise node to be connected. Perform connection operations on the new enterprise node and at least one enterprise node to be connected to obtain the updated current supply chain network.

4. The method according to claim 3, characterized in that, The process of obtaining newly added enterprises and their corresponding preset score thresholds includes: Obtain a list of newly added companies; wherein the list of newly added companies includes at least one candidate company and the competition score corresponding to each candidate company. The competition scores of at least one candidate enterprise in the list of new enterprises are sorted, and the candidate enterprise with the highest competition score in the sorting results is selected as the new enterprise. Obtain the preset score threshold corresponding to the newly added enterprise in the list of newly added enterprises.

5. The method according to claim 2, characterized in that, The step of performing a modeling operation corresponding to the current update type on the current supply chain network based on at least one competition score to obtain an updated current supply chain network includes: When the current update type is an exit type, the competition scores of at least one enterprise node corresponding to the current update type are sorted. The enterprise node with the lowest competition score in the ranking results is removed from the current supply chain network to obtain an updated current supply chain network.

6. The method according to any one of claims 1-5, characterized in that, The current supply chain network comprises at least two layers of node networks, and each node network contains at least one enterprise node. Accordingly, obtaining the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type includes: A second random value is generated, and the target node network is determined based on the second random value and the preset probability range corresponding to each node network. Based on the current update type, obtain the competition score of at least one enterprise node corresponding to the target node network.

7. A device for constructing a supply chain network, characterized in that, include: An initial supply chain network acquisition module is used to acquire an initial supply chain network as the current supply chain network; wherein, the initial supply chain network contains at least one enterprise node; The competition score acquisition module is used to determine the current update type and acquire the competition score of at least one enterprise node in the current supply chain network corresponding to the current update type; wherein the competition score is determined by using fuzzy hierarchical analysis based on the expert evaluation scores of the enterprise nodes; The current supply chain network update module is used to perform a modeling operation corresponding to the current update type on the current supply chain network based on at least one competition score, so as to obtain an updated current supply chain network; The target supply chain network determination module is used to repeatedly execute the step of determining the current update type based on the updated current supply chain network until a preset termination condition is met, and then the updated current supply chain network is taken as the target supply chain network. The device further includes: The priority relationship matrix group acquisition module is used to acquire, for each enterprise node in the current supply chain network, the expert evaluation score and priority relationship matrix group corresponding to the enterprise node; wherein, the priority relationship matrix group includes priority relationship matrices corresponding to at least two levels respectively, the levels include an expert evaluation level and at least one indicator level, and the priority relationship matrix is ​​used to characterize the relative importance between pairs of elements in the current level; The score weight determination module is used to convert each priority relation matrix in the priority relation matrix group into a fuzzy consistent matrix based on the fuzzy hierarchical transformation rule, and to determine the element weight of each level relative to the previous level based on the fuzzy hierarchical weight formula and each fuzzy consistent matrix, and to determine the score weight based on the element weight. The competition score determination module is used to determine the competition score corresponding to the enterprise node based on the expert evaluation score and the score weight.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for constructing a supply chain network as described in any one of claims 1-6.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the method for constructing a supply chain network as described in any one of claims 1-6.

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