A supply chain network optimization method and system

Abstract

The present invention discloses a supply chain network optimization method and system, the method comprising obtaining time consumption data of each node in the supply chain network, constructing a weighted directed graph of the supply chain network according to the time consumption data; obtaining the longest path length and average path length of the supply chain network according to the weighted directed graph, and obtaining the critical path of the supply chain network if one of the path lengths is higher than a preset path length threshold; calculating the time dependency of each node on the critical path according to the weighted directed graph to determine the critical nodes of the supply chain network; and optimizing the time of the critical nodes according to the structural characteristics and time consumption characteristics of the critical nodes. A graph theory model can be used to analyze the time dependency between each node in the supply chain network, identify the sensitivity of the supply chain network to time delay, and optimize the time of the key nodes in the supply chain network to optimize the time delay of the supply chain network.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain network, and in particular to a supply chain network optimization method and system. Background Art

[0002] In the supply chain network, time dependency refers to the degree to which the activities between the nodes in the supply chain are temporally interrelated. It not only considers the connection relationship between the nodes (such as the flow of goods and information), but also focuses on the impact of the time sequence and time interval of these activities on the entire supply chain. For example, the supplier's delivery time will directly affect the manufacturer's production plan, and the manufacturer's production progress will affect the distributor's receipt time and subsequent sales arrangements. In today's fierce market competition, customers have higher and higher requirements for the delivery time of products and services. Timely delivery can enhance customers' overall impression and experience of the company, help companies establish a good brand image, and enhance market competitiveness. In order to improve the overall efficiency of the entire supply chain network and enhance market competitiveness, it is necessary to clarify the time dependency of each node in the supply chain network and optimize the time delay of the supply chain network. Therefore, it is extremely important to identify the sensitivity of the supply chain network to time delay, calculate the time dependency of each node in the supply chain network, and optimize the key nodes in the supply chain network to optimize the time delay of the supply chain network. Summary of the invention

[0003] The present invention provides a supply chain network optimization method and system, which uses a graph theory model to analyze the time dependency between nodes in the supply chain network, identifies the sensitivity of the supply chain network to time delays, and optimizes key nodes in the supply chain network, thereby optimizing the time delay of the supply chain network and improving the overall efficiency of the supply chain network.

[0004] In order to achieve the above object, an embodiment of the present invention provides a supply chain network optimization method, including:

[0005] Acquire time consumption data of each node in the supply chain network, and construct a weighted directed graph of the supply chain network according to the time consumption data; wherein the time consumption data includes the delivery cycle, production cycle and transportation time data of each node;

[0006] Acquire the longest path length and the average path length of the supply chain network according to the weighted directed graph, and if the longest path length or the average path length is higher than a preset path length threshold, obtain the critical path of the supply chain network according to each path length of the supply chain network and the preset path length threshold;

[0007] Calculating the time dependency of each node on the critical path according to the weighted directed graph; determining the key nodes of the supply chain network according to the time dependency of each node;

[0008] The key nodes are time optimized according to the structural characteristics and time consumption characteristics of the key nodes to obtain an optimized supply chain network.

[0009] As an improvement of the above solution, the longest path length and the average path length of the supply chain network are obtained according to the weighted directed graph. If the longest path length or the average path length is higher than a preset path length threshold, the critical path of the supply chain network is obtained according to each path length of the supply chain network and the preset path length threshold, including:

[0010] Calculating each path length in the supply chain network according to the weighted directed graph, and obtaining the longest path length and the average path length of the supply chain network according to each path length;

[0011] If the longest path length or the average path length is greater than a preset path length threshold, each path greater than the preset path length threshold is used as a critical path in the supply chain network.

[0012] As an improvement of the above solution, the traversing and calculating each path length in the supply chain network according to the weighted directed graph, and obtaining the longest path length and the average path length of the supply chain network according to each path length, comprises:

[0013] Traversing each path in the supply chain network according to the weighted directed graph, and using the Dijkstra algorithm to calculate the time weight value between each node of each path;

[0014] Acquire a parallel path of each path according to the time weight value, and perform time weight accumulation operation on the parallel paths to obtain a path time consumption value of each path;

[0015] According to the path time consumption value, the node pair paths in the supply chain network are traversed, and the node pair paths are sorted by heap sorting to obtain an average time consumption value and a maximum time consumption value;

[0016] According to the average time consumption value and the maximum time consumption value, the longest path length and the average path length of the supply chain network are obtained.

[0017] As an improvement of the above solution, the step of calculating the time dependency of each node on the critical path according to the weighted directed graph; and determining the key nodes of the supply chain network according to the time dependency of each node includes:

[0018] Calculating the weighted in-degree and weighted out-degree of each node on the critical path according to the weighted directed graph;

[0019] According to the weighted in-degree and weighted out-degree of each node, the time dependency of each node on the critical path is obtained;

[0020] According to the comparison between the time dependency of each node and a preset time dependency value, the nodes with a time dependency value higher than the time dependency value are marked as key nodes of the supply chain network.

[0021] As an improvement of the above solution, the step of calculating the weighted in-degree and the weighted out-degree of each node on the critical path according to the weighted directed graph includes:

[0022] Traversing each node on the critical path according to the weighted directed graph, calculating the sum of the durations of all predecessor nodes of each node, and obtaining the weighted in-degree of each node;

[0023] The sum of the durations of all successor nodes of each node is calculated to obtain the weighted out-degree of each node.

[0024] As an improvement of the above solution, traversing each node on the critical path according to the weighted directed graph, calculating the sum of the durations of all predecessor nodes of each node, and obtaining the weighted in-degree of each node includes:

[0025] Acquire a node set of the critical path according to the weighted directed graph, and acquire a predecessor node of each node of the node set through an adjacency matrix;

[0026] According to the time characteristics of the predecessor node, the delivery cycle, production cycle and transportation time data of the predecessor node are accumulated by matrix multiplication operation to obtain the weighted in-degree of each node.

[0027] As an improvement of the above solution, the time optimization of the key nodes is performed according to the structural characteristics and time consumption characteristics of the key nodes to obtain an optimized supply chain network, including:

[0028] Acquire structural features and time consumption features of the key nodes;

[0029] According to the structural characteristics and time consumption characteristics, the delivery process data of the key nodes are obtained;

[0030] Formulate time optimization measures for the key nodes according to the delivery process data, and conduct time delay risk assessment on the time optimization measures;

[0031] The evaluated time optimization measures are used to optimize the time of the key nodes to obtain an optimized supply chain network.

[0032] As an improvement of the above solution, the step of obtaining the time consumption data of each node in the supply chain network and constructing a weighted directed graph of the supply chain network according to the time consumption data includes:

[0033] Obtain the time consumption data of each node in the supply chain network, and obtain the corresponding node time consumption weight value based on the order response time in the delivery cycle of each node and the material processing time in the production cycle;

[0034] According to the node time consumption weight value and the transportation time data, a corresponding transportation time weight value is obtained;

[0035] According to the node time consumption weight value and the transportation time weight value, a weighted directed graph of the supply chain network is constructed through a graph data structure.

[0036] As an improvement of the above solution, after obtaining the optimized supply chain network, the method further includes:

[0037] The longest path length and the average path length of the optimized supply chain network are obtained, and the optimized longest path length and the average path length are compared with the longest path length and the average path length to obtain an optimization evaluation result of the supply chain network.

[0038] In order to achieve the above object, an embodiment of the present invention provides a supply chain network optimization system, including:

[0039] A time data acquisition module, used to acquire the time consumption data of each node in the supply chain network, and construct a weighted directed graph of the supply chain network according to the time consumption data; wherein the time consumption data includes the delivery cycle, production cycle and transportation time data of each node;

[0040] A critical path determination module, configured to obtain the longest path length and the average path length of the supply chain network according to the weighted directed graph, and if the longest path length or the average path length is higher than a preset path length threshold, obtain the critical path of the supply chain network according to each path length of the supply chain network and the preset path length threshold;

[0041] A key node determination module, used to calculate the time dependency of each node on the key path according to the weighted directed graph; and determine the key nodes of the supply chain network according to the time dependency of each node;

[0042] The key node optimization module is used to optimize the time of the key nodes according to the structural characteristics and time consumption characteristics of the key nodes to obtain an optimized supply chain network.

[0043] Compared with the prior art, an optimization method and system for a supply chain network disclosed in an embodiment of the present invention obtains time consumption data of each node in the supply chain network, and constructs a weighted directed graph of the supply chain network according to the time consumption data; wherein the time consumption data includes the delivery cycle, production cycle and transportation time data of each node; the longest path length and the average path length of the supply chain network are obtained according to the weighted directed graph, and if the longest path length or the average path length is higher than a preset path length threshold, the critical path of the supply chain network is obtained according to each path length of the supply chain network and the preset path length threshold; the time dependency of each node on the critical path is calculated according to the weighted directed graph; the critical nodes of the supply chain network are determined according to the time dependency of each node; the critical nodes are time-optimized according to the structural characteristics and time consumption characteristics of the critical nodes to obtain an optimized supply chain network. It is possible to use graph theory models to analyze the time dependence between nodes in the supply chain network, identify the sensitivity of the supply chain network to time delays based on the longest path length and average path length, and optimize the time of key nodes in the supply chain network based on the structural characteristics and time consumption characteristics of the nodes to optimize the time delay of the supply chain network, thereby improving the overall efficiency of the supply chain network. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of a supply chain network optimization method provided by an embodiment of the present invention;

[0045] Figure 2 It is a structural diagram of a supply chain network optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] It should be noted that the terms "comprises" and "specifically" and any variations of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0048] See also Figure 1 , Figure 11 is a flow chart of a supply chain network optimization method provided by an embodiment of the present invention, wherein the supply chain network optimization method includes:

[0049] S1, obtaining time consumption data of each node in the supply chain network, and constructing a weighted directed graph of the supply chain network according to the time consumption data; wherein the time consumption data includes the delivery cycle, production cycle and transportation time data of each node;

[0050] S2, obtaining the longest path length and the average path length of the supply chain network according to the weighted directed graph, and if the longest path length or the average path length is higher than a preset path length threshold, obtaining the critical path of the supply chain network according to each path length of the supply chain network and the preset path length threshold;

[0051] S3, calculating the time dependency of each node on the critical path according to the weighted directed graph; determining the key nodes of the supply chain network according to the time dependency of each node;

[0052] S4, performing time optimization on the key nodes according to the structural characteristics and time consumption characteristics of the key nodes to obtain an optimized supply chain network.

[0053] Exemplarily, the optimization method of the supply chain network described in the embodiment of the present invention is implemented by a supply chain network server, which can interact with users and the supply chain data platform. The supply chain network server obtains the time consumption data of each node in the supply chain network (such as delivery cycle, production cycle and transportation time data), and constructs a weighted directed graph of the supply chain network based on the time consumption data. In the supply chain network, each supply path is found, and the time consumption of each supply path is calculated through a weighted directed graph. The maximum time consumption is used as the longest path length of the supply chain network, and all time consumptions are weighted averaged to obtain the average path length of the supply chain network; the longest path length or the average path length is compared with a preset path length threshold (the preset path length threshold can be set as needed, such as taking the median of the time consumption data of the previous supply path as the preset path length threshold). If the longest path length or the average path length is higher than the preset path length threshold, it is considered that the supply chain network is sensitive to time delay and the supply chain network needs to be optimized. The network structure of the supply chain network is optimized, the critical path of the supply chain network is identified (each path that is longer than a preset path length threshold is taken as the critical path in the supply chain network), and the time dependency of each node on the critical path is calculated according to the weighted directed graph; the key nodes of the supply chain network are determined according to the time dependency of each node, and the key nodes are time-optimized according to the structural characteristics and time consumption characteristics of the key nodes to obtain an optimized supply chain network; the graph theory model can be used to analyze the time dependency between each node in the supply chain network, and the sensitivity of the supply chain network to time delay can be identified to time optimize the key nodes in the supply chain network.

[0054] Specifically, the step S1 includes:

[0055] S11, obtaining the time consumption data of each node in the supply chain network, and obtaining the corresponding node time consumption weight value according to the order response time in the delivery cycle of each node and the material processing time in the production cycle;

[0056] S12, obtaining a corresponding transportation time weight value according to the node time consumption weight value and the transportation time data;

[0057] S13, constructing a weighted directed graph of the supply chain network through a graph data structure according to the node time consumption weight value and the transportation time weight value.

[0058] Specifically, the delivery cycle and production cycle of each node in the supply chain network are obtained from the supplier node database, and the node time consumption weight value of each node is calculated using the Dijkstra algorithm according to the order response time in the delivery cycle of each node and the material processing time in the production cycle; according to the node time consumption weight value, the transportation time data of each node is extracted through the supply chain database, and the transportation time calculation formula is used to obtain the transmission time between each node and the adjacent node, thereby obtaining the transportation time weight value of each node, and recording it in the adjacency table, and marking the connection relationship between the corresponding nodes in the adjacency matrix; according to the node time consumption weight value and the transportation time weight value of each node, a weighted directed graph of the supply chain network is constructed through a graph data structure, wherein the weight of the node is the node time consumption, the weight of the edge is the edge time consumption, and the node hierarchical relationship and the transmission direction of the edge are recorded in the weighted directed graph; if there are parallel paths in the weighted directed graph, the Floyd algorithm is used to calculate the sum of the time consumption of all paths between any two points, and the minimum time consumption in all paths between the two points is stored through a red-black tree data structure.

[0059] Exemplarily, according to the node time consumption weight value in the adjacency matrix and the transportation time weight value in the adjacency table, the supplier nodes are hierarchically sorted according to the order of material delivery, and a weighted directed graph of the supply chain is constructed through a graph data structure, and the node hierarchical relationship and the transmission direction of the edge are recorded in the weighted directed graph. For the weighted directed graph of the supply chain, the sum of the time consumption of all possible paths between any two points is calculated according to the Floyd algorithm, and if it is determined that there is a parallel supply path, the minimum time consumption value is selected as the final time consumption value between the two points, and the final time consumption value is stored through a red-black tree data structure. The delivery cycle in the supply chain network node involves the order response time and the material processing time in the production cycle. The order response time includes two links: order review time and material preparation time. The node time series data is weighted by the Dijkstra algorithm. For example, the order review stage takes 6 hours and the material preparation stage takes 12 hours, so the total order response time is calculated to be 18 hours. The material processing cycle involves three stages: machine debugging, raw material processing, and quality inspection. The machine debugging stage takes 4 hours, the raw material processing stage takes 24 hours, and the quality inspection stage takes 8 hours. The total material processing time is 36 hours. The transportation time is calculated based on the transportation distance and transportation rate. The transportation distance is obtained by GPS positioning to obtain the actual distance between nodes. The transportation rate is determined according to different means of transportation. The land transportation rate is 40 km / h, the railway transportation rate is 80 km / h, and the air transportation rate is 800 km / h. For two nodes 500 km apart, it takes 12.5 hours to use land transportation, 6.25 hours to use railway transportation, and 0.625 hours to use air transportation. The time consumption of different transportation modes corresponding to multiple edges is recorded in the adjacency table.

[0060] In actual application scenarios, if the supply chain network of a medium-sized automobile manufacturer includes 50 raw material suppliers, 100 parts suppliers, and 10 final assembly suppliers, and the suppliers are distributed throughout the country and transport materials by land, rail, air, and other means, the constructed supply chain weighted directed graph contains 160 nodes and 500 edges. The average delivery cycle from raw material suppliers to final assembly suppliers is calculated to be 168 hours, of which node time consumption accounts for 40% and edge transmission time consumption accounts for 60%. The overall time consumption can be reduced by adjusting the supplier layout and transportation methods.

[0061] It is worth noting that the supply chain network nodes are layered according to the order of material delivery. The raw material supplier is located in the first level, the parts supplier is located in the second level, and the final assembly supplier is located in the third level. In the weighted directed graph, the material flow direction is indicated by the direction of the arrow. The raw material supplier delivers raw materials to the parts supplier, and the parts supplier delivers semi-finished products to the final assembly supplier. If the raw material supplier directly supplies the final assembly supplier, a cross-level connection is added to the graph. The time consumption of all paths between any two points is calculated. There are multiple parallel supply paths from the raw material supplier to the final assembly supplier. For example, path (1) takes a long time to transfer through the parts supplier but has a lower cost, and path (2) takes a shorter time to directly supply but has a higher cost. The time consumption values ​​of different paths are stored in a red-black tree and the shortest path is selected. In the supply chain optimization, the time consumption and cost factors are combined to determine the optimal supply path.

[0062] Specifically, step S2 includes:

[0063] S21, calculating the length of each path in the supply chain network according to the weighted directed graph, and obtaining the longest path length and the average path length of the supply chain network according to each path length;

[0064] Exemplarily, each path in the supply chain network is traversed according to the weighted directed graph, and each edge of each path is cumulatively added to obtain the length of each path; all path lengths are sorted to obtain the longest path length of the weighted directed graph; and all path lengths are weighted summed to obtain the average path length of the weighted directed graph.

[0065] S22: If the longest path length or the average path length is greater than a preset path length threshold, each path greater than the preset path length threshold is used as a critical path in the supply chain network.

[0066] More specifically, step S21 includes:

[0067] S211, traversing each path in the supply chain network according to the weighted directed graph, and using the Dijkstra algorithm to calculate the time weight value between each node of each path;

[0068] S212, obtaining a parallel path of each path according to the time weight value, and performing a time weight accumulation operation on the parallel paths to obtain a path time consumption value of each path;

[0069] S213, traversing the node pair paths in the supply chain network according to the path time consumption value, sorting the node pair paths by heap sorting, and obtaining an average time consumption value and a maximum time consumption value;

[0070] S214: Obtain the longest path length and the average path length of the supply chain network according to the average time consumption value and the maximum time consumption value.

[0071] Specifically, according to the weighted directed graph, each path in the supply chain network is traversed, and the time weight value between each node of each path is calculated by using the Dijkstra algorithm; according to the time weight value, the parallel supply path of each path is obtained by breadth-first search, and the time weight accumulation operation is performed on the parallel supply path to obtain the path time consumption value of each path; according to the path time consumption value, the node pair paths in the supply chain network are traversed by depth-first search, and the node pair paths are sorted by heap sorting to obtain the average time consumption value and the maximum time consumption value; thereby obtaining the longest path length and the average path length of the supply chain network. If the path time consumption value of each path exceeds the preset time threshold parameter, the corresponding path is used as the critical path in the supply chain network. For example, the weighted directed graph is traversed and calculated by the longest path algorithm, and if the path time accumulation value exceeds the preset time threshold, the critical path is obtained, and the node identifiers and edge identifiers on the critical path are recorded by using a graph database.

[0072] Exemplarily, according to the time weight data between nodes in the weighted directed graph of the supply chain network, the important node identifiers are read from the supplier database, the Dijkstra algorithm is used to calculate the time consumption values ​​between the marked important nodes, all parallel supply paths are obtained through breadth-first search, the time weight values ​​of each node and the edge time weight values ​​on each path are accumulated, and the longest time consumption is recorded as the critical time consumption value (the longest path length). The connection relationship between node pairs is extracted from the adjacency matrix, and according to the nodes marked with the critical time consumption value, all node pair paths are traversed using depth-first search, the time weight values ​​of each path are accumulated, and the median time consumption value of all paths is obtained through heap sorting to obtain the average time consumption value (average path length) of the supply chain network. The time threshold parameter corresponding to the node is read from the time threshold database. If the critical time consumption value exceeds the time threshold parameter, the corresponding node pair is marked as a high-latency node pair (critical path), and the high-latency node pair data is recorded through a red-black tree. For the high-latency node pair data, the connection relationship between nodes is optimized and reconstructed according to the minimum spanning tree algorithm. If a parallel supply path exists between the node pairs, the path with the minimum time consumption is retained, redundant path connections are deleted, and the optimized weighted directed graph data is stored in an adjacency table.

[0073] It is worth noting that the important nodes in the supply chain network include core raw material suppliers, large manufacturers, and regional logistics centers. When calculating the time consumption values ​​between these important nodes, there are multiple parallel supply paths. For example, in the supply path from the raw material supplier to the manufacturer, path 1 is transferred through the regional logistics center, and path 2 bypasses the logistics center for direct transportation. The time consumption of the two paths is calculated by the Dijkstra algorithm. The time consumption of path 1 is 24 hours of production time of the raw material supplier plus 12 hours of transportation time to the logistics center plus 8 hours of time from the logistics center to the manufacturer. The time consumption of path 2 is 24 hours of production time of the raw material supplier plus 16 hours of direct transportation time.

[0074] The distribution of path time consumption between node pairs presents different characteristics. The path time consumption from suppliers to logistics centers is relatively stable, with a fluctuation range of 10 to 14 hours. The path time consumption from logistics centers to manufacturers varies greatly. Affected by the mode of transportation, land transportation takes 8 to 12 hours, and air transportation takes 2 to 4 hours. All node pairs are traversed and counted, and the median time consumption value is obtained by heap sorting to reflect the overall time level of the supply chain network. Differentiated time threshold parameters are set for different types of nodes. The time threshold from raw material suppliers to manufacturers is 48 hours, the time threshold from manufacturers to logistics centers is 24 hours, and the time threshold from logistics centers to retailers is 36 hours. If the actual path time consumption exceeds the threshold, it is marked as a high-latency node pair. For example, the actual time consumption from raw material suppliers to manufacturers is 72 hours, which exceeds the 48-hour threshold and is determined to be a high-latency node pair. During the optimization process of the supply chain network structure, high-latency nodes are optimized as the priority. In the supply path from raw material suppliers to manufacturers, the minimum spanning tree algorithm is used to delete the 72-hour high-latency path and retain the 44-hour low-latency path. Logistics center nodes are added in supplier concentration areas to reduce transportation time, and alternative suppliers are arranged around manufacturers to provide parallel supply channels, forming a network structure with multi-point supply and nearby distribution.

[0075] For example, in the supply chain network of a large manufacturing enterprise, there are 200 supplier nodes, 50 logistics center nodes, and 20 manufacturer nodes. Through critical path analysis, it was found that the supply path time from suppliers in the Pearl River Delta to manufacturers in North China consumed 96 hours, which was significantly higher than the 48-hour time threshold. After optimization, local suppliers were developed in North China and supporting logistics centers were built. The supply path time was reduced to 36 hours, while the original supply path was retained as an alternative channel.

[0076] Specifically, the step S3 includes:

[0077] S31, calculating the weighted in-degree and weighted out-degree of each node on the critical path according to the weighted directed graph;

[0078] S32, obtaining the time dependency of each node on the critical path according to the weighted in-degree and weighted out-degree of each node;

[0079] S33, comparing the time dependency of each node with a preset time dependency value, marking the nodes with a time dependency value higher than the time dependency value as key nodes of the supply chain network.

[0080] More specifically, the step S31 includes:

[0081] S311, traversing each node on the critical path according to the weighted directed graph, calculating the sum of the durations of all predecessor nodes of each node, and obtaining the weighted in-degree of each node;

[0082] S312, calculating the sum of the durations of all successor nodes of each node, and obtaining the weighted out-degree of each node.

[0083] Exemplarily, the identifier of each node of the critical path is obtained according to the critical path of the supply chain network, and the delivery cycle, production cycle, and transportation time data of each node are extracted from the node database; the predecessor node set and the successor node set of each node are read through the adjacency matrix, and the delivery cycle, production cycle, and transportation time data of the predecessor node and the successor node are weighted summed up, and the in-degree duration value (weighted in-degree) and out-degree duration value (weighted out-degree) of each node are recorded using a graph database to obtain the time dependency of each node on the critical path; the in-degree duration value and out-degree duration value of each node are read from the graph database, and if the in-degree duration value is greater than the out-degree duration value, the upstream dependency is recorded, and if the out-degree duration value is greater than the in-degree duration value, the downstream dependency is recorded.

[0084] For example, the duration of the engine manufacturer node in the automotive parts supply chain includes three links: order processing time of 12 hours, raw material preparation time of 24 hours, and production and processing time of 72 hours. The predecessor nodes of this node include cylinder suppliers, piston suppliers, and camshaft suppliers. The adjacency matrix calculation shows that the cumulative duration of the predecessor nodes is 264 hours, of which the cylinder supplier lasts for 96 hours, the piston supplier lasts for 84 hours, and the camshaft supplier lasts for 84 hours. The successor nodes of the engine manufacturer node include vehicle manufacturers and spare parts centers. The duration of the vehicle manufacturer is 144 hours, including 96 hours of assembly time, 24 hours of debugging time, and 24 hours of testing time. The duration of the spare parts center is 72 hours, including 24 hours of warehousing time, 36 hours of storage time, and 12 hours of outbound time. The cumulative duration of the two successor nodes is 216 hours. When calculating the time dependency of the engine manufacturer node, the ratio of the in-degree duration value of 264 hours to the out-degree duration value of 216 hours is 1.22. A ratio greater than 1 indicates that the node is more sensitive to upstream time changes. The impact of upstream supplier delays on the node is greater than the impact of the node on downstream customers, and it is recorded as an upstream dependent node. In the entire automotive supply chain network, 80 key nodes are divided into strong dependency groups and weak dependency groups through k-means clustering. The strong dependency group contains 35 nodes with an average time dependency of 1.45, and the weak dependency group contains 45 nodes with an average time dependency of 0.85. The dependency of the engine manufacturer node is 1.22, which is lower than the mean of the strong dependency group but significantly higher than the mean of the weak dependency group, and is divided into a strong dependency group node.

[0085] For example, the home appliance supply chain network presents different time dependency characteristics. The cumulative duration of the predecessor node of the compressor manufacturer node is 168 hours, the cumulative duration of the successor node is 96 hours, and the time dependency is 1.75, which is significantly higher than the strong dependency group mean of 1.45, reflecting the key time transmission role of this node in the refrigeration equipment supply chain. The chip packaging factory node in the electronic component supply chain shows the opposite time dependency characteristics. The cumulative duration of the predecessor node is 72 hours, the cumulative duration of the successor node is 168 hours, and the time dependency is 0.43, which is lower than the weak dependency group mean of 0.85, indicating that this node has a strong time buffering ability and is relatively less affected by upstream delays. The API manufacturer node in the pharmaceutical supply chain network has unique time characteristics. The cumulative duration of the predecessor node and the successor node are similar, 144 hours and 156 hours respectively. The time dependency is 0.92, close to 1, and is at the boundary of the strong and weak dependency groups, reflecting the balanced transmission role of this node in the supply chain.

[0086] More specifically, the step S311 includes:

[0087] Acquire a node set of the critical path according to the weighted directed graph, and acquire a predecessor node of each node of the node set through an adjacency matrix;

[0088] According to the time characteristics of the predecessor node, the delivery cycle, production cycle and transportation time data of the predecessor node are accumulated by matrix multiplication operation to obtain the weighted in-degree of each node.

[0089] Exemplarily, a set of critical path nodes is obtained based on a weighted directed graph of a supply chain network, the node time characteristics are calculated using an adjacency matrix, and the three indicator data of the node's delivery cycle, production cycle, and transportation time are accumulated using matrix multiplication operations to obtain the node's weighted in-degree time value; for the critical path node set, the time weight data between nodes is read from a graph database, and vector addition operations are used to accumulate the time consumption weights of the node's outgoing edges to obtain the node's weighted out-degree time value; based on the node's weighted in-degree time value and weighted out-degree time value, a weighted ratio of the two time values ​​is calculated using a weighted average algorithm, and if the weighted in-degree time value ratio exceeds a preset threshold, the node's upstream time dependency is recorded, and if the weighted out-degree time value ratio exceeds a preset threshold, the node's downstream time dependency is recorded; for the node time dependency data, a k-means clustering algorithm is used to group the node time dependencies, and if the node time dependency is higher than the group mean, it is marked as a strong dependency node.

[0090] For example, in the automobile manufacturing supply chain network, the time characteristics of the key path nodes cover three dimensions: delivery cycle, production cycle, and transportation time. The delivery cycle of the engine supplier node is 24 hours, the production cycle is 72 hours, and the transportation time is 36 hours. The node weighted in-degree time value obtained by adjacency matrix calculation is 132 hours, which reflects the cumulative effect of the engine supplier being affected by the time of the upstream parts supplier. In the process of calculating the node weighted out-degree time value, the time weight data of the engine supplier providing products to the vehicle manufacturer shows that the delivery time accounts for 30%, the production time accounts for 50%, and the transportation time accounts for 20%. The weighted out-degree time value obtained by vector addition operation is 144 hours, which reflects the degree of time influence of the engine supplier on the downstream vehicle manufacturer. In the calculation of node time dependency, the weighted in-degree time value of the engine supplier is 132 hours, accounting for 47.8% of the total time, and the weighted out-degree time value is 144 hours, accounting for 52.2% of the total time, which is higher than the preset threshold of 45%, indicating that the node is sensitive to both upstream and downstream time changes and belongs to a bidirectional strong dependency node, which is recorded in the red-black tree for subsequent optimization.

[0091] In the case of home appliance manufacturing supply chain, the compressor supplier node is divided into a strong dependency group through k-means clustering, with an average time dependency of 65% within the group and an actual time dependency of 72%, indicating that the node plays a key role in time transmission in the supply chain. Delays in upstream raw material supply or fluctuations in orders from downstream complete machine manufacturers will significantly affect the production rhythm of the node. The electronic component supply chain network presents different time dependency characteristics. The weighted in-degree time value of the chip manufacturer node is 168 hours, the weighted out-degree time value is 48 hours, and the in-degree time accounts for 77.8%, which is much higher than the out-degree time of 22.2%. It is a typical upstream dependency node, and the production process is highly sensitive to the raw material supply time. In the pharmaceutical manufacturing supply chain, the weighted in-degree time value of the API supplier node is 96 hours, the weighted out-degree time value is 240 hours, and the out-degree time accounts for 71.4%, which is significantly higher than the in-degree time of 28.6%. It is a downstream dependency node. The time fluctuation of this node will continue to be transmitted downstream along the supply chain, affecting the delivery time of preparation manufacturers and drug distributors. In the practice of chemical product supply chain, the delivery cycle of the intermediate supplier node is 36 hours, the production cycle is 144 hours, and the transportation time is 48 hours. Through time characteristic analysis, it is found that this node is not only restricted by the upstream raw material time, but also affects the downstream product supply. It is located in the key link of time transmission. In supply chain optimization, the time compression space of this type of node should be given priority.

[0092] Specifically, the step S4 includes:

[0093] S41, obtaining structural features and time consumption features of the key nodes;

[0094] S42, obtaining the delivery process data of the key node according to the structural characteristics and the time consumption characteristics;

[0095] S43, formulating time optimization measures for the key nodes according to the delivery process data, and performing time delay risk assessment on the time optimization measures;

[0096] S44, optimizing the time of the key nodes using the evaluated time optimization measures to obtain an optimized supply chain network.

[0097] Exemplarily, the structural characteristics and time consumption characteristics of the key nodes are obtained, the delivery process data of the key nodes are obtained, and time optimization measures for the key nodes are formulated according to the delivery process data. According to the time optimization measures, a key path compression algorithm is used to perform delivery cycle compression calculation on the node time data in the delivery process data, and transportation time compression calculation on the edge time data in the delivery process data, and the compressed time value is recorded through a graph database; for the compressed time value, the shortest path algorithm is used to calculate the target time length of the key path, and it is determined according to a preset time threshold that if the target time length is greater than the preset time threshold, the path data to be optimized is obtained; node identifiers and edge identifiers are extracted from the path data to be optimized, and a quantitative grading standard is used to perform time delay risk assessment on the node identifiers and edge identifiers, and the time optimization measures are recorded through a red-black tree; according to the time optimization measures, an iterative depth-first search algorithm is used to perform optimization combination search, and if it is determined that the optimization combination target time length is less than the preset time threshold, the evaluated time optimization measures are stored through a graph database, and the key nodes are time optimized using the evaluated time optimization measures to obtain an optimized supply chain network.

[0098] For example, in the automobile manufacturing supply chain, the initial time data of the engine supplier node showed that the delivery cycle was 24 hours, the production cycle was 96 hours, and the transportation cycle was 48 hours. Through the key path compression algorithm, each link was optimized, and the order processing time was compressed from 8 hours to 4 hours, the material preparation time was compressed from 16 hours to 12 hours, the production commissioning time was compressed from 24 hours to 16 hours, the processing time was compressed from 48 hours to 36 hours, and the quality inspection time was compressed from 24 hours to 16 hours. The total time of the optimized engine supplier node was reduced from 168 hours to 84 hours, the connected transportation path was optimized from the original 48 hours to 36 hours, the target time length of the entire key path was reduced from the original 432 hours to 312 hours, and the average time length was reduced from 384 hours to 288 hours. Compared with the preset 336-hour threshold, there is still room for optimization. The path from the engine supplier to the vehicle manufacturer is recorded as the path to be optimized. When classifying the risks of the optimized paths, the production cycle fluctuation of the engine supplier node exceeds 24 hours, which is classified as the first-level time delay risk, the delay frequency of the transportation path exceeds 15%, which is classified as the second-level time delay risk, and the delivery cycle fluctuation of the vehicle manufacturer node is within 12 hours, which is classified as the third-level time delay risk, forming differentiated optimization measures. In the process of combining optimization measures, the engine supplier adopts a parallel production plan, conducts quality inspection and packaging simultaneously, adds a railway transportation alternative for the transportation path, and the vehicle manufacturer sets up emergency inventory. The combination of the three measures reduces the target time length to 264 hours, which is lower than the preset threshold of 336 hours.

[0099] More specifically, the step S41 includes:

[0100] S411, according to the connection relationship of the key node in the weighted directed graph, calculate the sum of the incoming edge weights and the sum of the outgoing edge weights of the key node, and obtain the forward dependency and backward dependency of the key node according to the sum of the incoming edge weights and the sum of the outgoing edge weights;

[0101] S412, traversing the key node according to the forward dependency and the backward dependency, obtaining the number of paths passing through the key node by counting, and determining the structural characteristics of the key node according to the ratio of the number of paths to the total paths of the weighted directed graph;

[0102] S413: Obtain the time consumption characteristics of the key node according to the delivery cycle and production cycle of the key node.

[0103] Exemplarily, based on the connection relationship data of the key node in the adjacency matrix, a network centrality algorithm is used to calculate the sum of the node's incoming edge weights and the sum of the outgoing edge weights, and the forward dependency and backward dependency of the node are marked according to the weight sum; the shortest path algorithm is used to traverse the key node, and the number of paths passing through the key node is statistically obtained, and the structural characteristics of the key node are determined according to the proportion of the number of paths to the total paths; based on the delivery cycle and production cycle of the key node, the time consumption characteristics of the key node are obtained.

[0104] For example, the engine supplier node connects 5 upstream parts suppliers and 3 downstream vehicle manufacturers, with a total weight of 240 hours for the incoming edge time and 144 hours for the outgoing edge time. The forward dependency of 1.67 is greater than the backward dependency, indicating that the node is more sensitive to changes in upstream supply time. The gearbox supplier node connects 4 upstream suppliers and 4 downstream manufacturers, with a total weight of 192 hours for the incoming edge time and 168 hours for the outgoing edge time. The forward dependency of 1.14 is close to the backward dependency. There are 180 shortest paths between node pairs in the supply chain network, of which 72 paths pass through the engine supplier node, accounting for 40%, and 54 paths pass through the gearbox supplier node, accounting for 30%. The paths of the two high-dependency nodes (key nodes) account for more than 20% of the average level, reflecting a strong time transfer effect. The order processing time of the engine supplier node is 24 hours, the production and processing time is 96 hours, and the transportation and delivery time is 48 hours. The order processing time of the gearbox supplier node is 18 hours, the production and processing time is 72 hours, and the transportation and delivery time is 36 hours. Through hierarchical clustering, the two nodes are divided into the long-cycle processing group, combined with their high path proportion characteristics.

[0105] In step S43, the standard operating process tool is used to split the order processing, material preparation, packaging and distribution links in the delivery process, and the delivery links are reorganized according to the node capacity and resource restrictions. If the adjacent links have overlapping time periods, the time window integration is performed, and the compressed delivery link data and related resource data are recorded through the red-black tree. For the production period in the delivery link data, the machine debugging, raw material processing, and quality inspection processes are decomposed by the operation flow chart tool, and the Dijkstra algorithm is used to calculate the shortest connection path between the processes. If it is judged that there is an idle time window between the processes, the process sequence is reorganized, and the optimized production process data is recorded using a heap structure. The transportation data on the critical path is read from the graph database, and the vehicle path planning algorithm is used to calculate the travel time of multiple transportation routes between nodes. The transportation routes are screened according to the load and road condition restrictions of the transportation tools. If the travel time of the transportation route is less than the preset threshold, the route is retained, and the screened transportation route data and scheduling time data are recorded through the adjacency table.

[0106] For example, in the delivery process of the critical path, order processing includes three links: order entry, credit check, and inventory confirmation. The standard operating process shows that order entry takes 2 hours, credit check takes 4 hours, and inventory confirmation takes 2 hours. Through time window integration, credit check and inventory confirmation are processed in parallel, and the total delivery time is compressed to 4 hours, while considering the workload constraints of credit personnel and inventory management personnel. In the process of production process optimization, the machine debugging link includes 0.5 hours of equipment preheating, 0.5 hours of parameter setting, and 1 hour of trial operation. The raw material processing link includes 1 hour of loading, 6 hours of processing, and 1 hour of unloading. The quality inspection link includes 0.5 hours of sampling, 2 hours of testing, and 0.5 hours of report generation. The shortest connection path algorithm is used to achieve process optimization, and equipment preheating and raw material preparation are processed in parallel to achieve production cycle compression. In the transportation route planning, there are two modes of transportation from the production plant to the distribution center: highway transportation and rail transportation. The highway transportation distance is 800 kilometers, the average speed is 80 kilometers per hour, and the travel time is 10 hours. The rail transportation distance is 1,000 kilometers, the average speed is 100 kilometers per hour, and the travel time is 10 hours. Considering the volume and weight restrictions of the goods, the single transportation volume of the highway is 20 tons, and the single transportation volume of the rail is 100 tons. The appropriate transportation method is selected based on the actual shipment volume. In the supply chain network case of a large home appliance manufacturer, the critical path involves three core nodes: motor suppliers, complete machine manufacturers, and regional distributors. Through delivery process optimization, the order response time is compressed from 24 hours to 12 hours, through production process optimization, the complete machine assembly time is compressed from 48 hours to 36 hours, and through transportation scheduling optimization, the logistics distribution time is compressed from 72 hours to 48 hours, realizing the overall time compression of the key path of the supply chain and ensuring the timeliness of supply. In actual operations, delivery process optimization takes into account the impact of node production capacity fluctuations, sets up buffer inventory to deal with sudden orders, reserves equipment maintenance time windows during production process optimization, considers seasonal road condition changes in transportation scheduling optimization, establishes emergency alternative plans, and achieves stable operation of key paths of the supply chain.

[0107] Furthermore, after obtaining the optimized supply chain network, the method further includes:

[0108] S5, obtaining the longest path length and the average path length of the optimized supply chain network, comparing the optimized longest path length and the average path length with the longest path length and the average path length, and obtaining an optimization evaluation result of the supply chain network.

[0109] Exemplarily, a weighted average algorithm is used to accumulate the node time consumption data and the edge time consumption data in the optimized supply chain network to obtain the optimized target time length and the average time length; based on the target time length and the average time length, the node time improvement value is read from the adjacency matrix, and the delivery time improvement rate, production time improvement rate, and transportation time improvement rate are calculated for the node time improvement value according to the quantitative scoring standard, and the node time comparison data is stored in a red-black tree; based on the time difference in the node time comparison data, a hierarchical analysis method is used to quantitatively calculate the time compression contribution of the time optimization measure, and the time improvement effect score of the time optimization measure is recorded through an adjacency table; for the time improvement effect score, a multidimensional evaluation system is used to comprehensively score the network structure change index, the node time change index, and the edge time change index to obtain the optimization evaluation result of the supply chain network.

[0110] See also Figure 2 , Figure 2 1 is a schematic diagram of a supply chain network optimization system 10 provided in an embodiment of the present invention. The supply chain network optimization system 10 includes:

[0111] The time data acquisition module 11 is used to acquire the time consumption data of each node in the supply chain network, and construct a weighted directed graph of the supply chain network according to the time consumption data; wherein the time consumption data includes the delivery cycle, production cycle and transportation time data of each node;

[0112] A critical path determination module 12 is used to obtain the longest path length and the average path length of the supply chain network according to the weighted directed graph, and if the longest path length or the average path length is higher than a preset path length threshold, then obtain the critical path of the supply chain network according to each path length of the supply chain network and the preset path length threshold;

[0113] A key node determination module 13 is used to calculate the time dependency of each node on the key path according to the weighted directed graph; and determine the key nodes of the supply chain network according to the time dependency of each node;

[0114] The key node optimization module 14 is used to perform time optimization on the key nodes according to the structural characteristics and time consumption characteristics of the key nodes to obtain an optimized supply chain network.

[0115] Furthermore, the supply chain network optimization system 10 also includes:

[0116] The optimization effect evaluation module is used to obtain the longest path length and average path length of the optimized supply chain network, compare the optimized longest path length and average path length with the longest path length and average path length, and obtain the optimization evaluation result of the supply chain network.

[0117] A supply chain network optimization system 10 provided in an embodiment of the present invention can implement all processes of the supply chain network optimization method of the above-mentioned embodiment. The functions of each module in the system and the technical effects achieved are respectively the same as the functions of the supply chain network optimization method of the above-mentioned embodiment and the technical effects achieved, which will not be repeated here.

[0118] The embodiment of the present invention discloses a supply chain network optimization method and system, which obtains the time consumption data of each node in the supply chain network, and constructs a weighted directed graph of the supply chain network according to the time consumption data; wherein the time consumption data includes the delivery cycle, production cycle and transportation time data of each node; obtains the longest path length and average path length of the supply chain network according to the weighted directed graph, and if the longest path length or the average path length is higher than a preset path length threshold, obtains the critical path of the supply chain network according to each path length of the supply chain network and the preset path length threshold; calculates the time dependency of each node on the critical path according to the weighted directed graph; determines the key nodes of the supply chain network according to the time dependency of each node; optimizes the key nodes according to the structural characteristics and time consumption characteristics of the key nodes, and obtains the optimized supply chain network. The graph theory model can be used to analyze the time dependency between each node in the supply chain network, identify the sensitivity of the supply chain network to time delay according to the longest path length and the average path length, and optimize the key nodes in the supply chain network according to the structural characteristics and time consumption characteristics of the nodes, so as to optimize the time delay of the supply chain network, thereby improving the overall efficiency of the supply chain network.

[0119] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A supply chain network optimization method, characterized in that: include: Obtain the time consumption data of each node in the supply chain network, and obtain the corresponding node time consumption weight value according to the order response time in the delivery cycle and the material processing time in the production cycle of each node; obtain the corresponding transportation time weight value according to the node time consumption weight value and the transportation time data; construct a weighted directed graph of the supply chain network through a graph data structure according to the node time consumption weight value and the transportation time weight value; wherein the time consumption data includes the delivery cycle, production cycle and transportation time data of each node; Traversing each path in the supply chain network according to the weighted directed graph, using the Dijkstra algorithm to calculate the time weight value between each node of each path; obtaining the parallel path of each path according to the time weight value, and performing time weight accumulation operation on the parallel path to obtain the path time consumption value of each path; traversing the paths between the node pairs in the supply chain network according to the path time consumption value, sorting the paths between the node pairs by heap sorting, and obtaining the average time consumption value and the maximum time consumption value; obtaining the longest path length and the average path length of the supply chain network according to the average time consumption value and the maximum time consumption value; if the longest path length or the average path length is greater than a preset path length threshold, each path greater than the preset path length threshold is used as a critical path in the supply chain network; The node set of the critical path is obtained according to the weighted directed graph, and the predecessor node of each node of the node set is obtained through the adjacency matrix; according to the time characteristics of the predecessor node, the delivery cycle, production cycle and transportation time data of the predecessor node are accumulated by vector addition operation to obtain the weighted in-degree of each node; the sum of the duration of all successor nodes of each node is calculated to obtain the weighted out-degree of each node; the time dependency of each node on the critical path is obtained by dividing the weighted in-degree of each node by the weighted out-degree; according to the time dependency of each node, the time dependency is compared with the preset time dependency value, and the nodes with a time dependency value higher than the time dependency value are marked as key nodes of the supply chain network; The key nodes are time optimized according to the structural characteristics and time consumption characteristics of the key nodes to obtain an optimized supply chain network.

2. The supply chain network optimization method according to claim 1, characterized in that: The step of optimizing the time of the key nodes according to the structural characteristics and time consumption characteristics of the key nodes to obtain an optimized supply chain network includes: Acquire structural features and time consumption features of the key nodes; According to the structural characteristics and time consumption characteristics, the delivery process data of the key nodes are obtained; Formulate time optimization measures for the key nodes according to the delivery process data, and conduct time delay risk assessment on the time optimization measures; The evaluated time optimization measures are used to optimize the time of the key nodes to obtain an optimized supply chain network.

3. The supply chain network optimization method according to claim 1, characterized in that: After obtaining the optimized supply chain network, the method further includes: The longest path length and the average path length of the optimized supply chain network are obtained, and the optimized longest path length and the average path length are compared with the longest path length and the average path length to obtain an optimization evaluation result of the supply chain network.

4. A supply chain network optimization system, characterized in that: include: The time data acquisition module is used to acquire the time consumption data of each node in the supply chain network, and obtain the corresponding node time consumption weight value according to the order response time in the delivery cycle and the material processing time in the production cycle of each node; obtain the corresponding transportation time weight value according to the node time consumption weight value and the transportation time data; and construct a weighted directed graph of the supply chain network through a graph data structure according to the node time consumption weight value and the transportation time weight value; wherein the time consumption data includes the delivery cycle, production cycle and transportation time data of each node; A critical path determination module is used to traverse each path in the supply chain network according to the weighted directed graph, and use the Dijkstra algorithm to calculate the time weight value between each node of each path; obtain the parallel path of each path according to the time weight value, and perform time weight accumulation operation on the parallel path to obtain the path time consumption value of each path; traverse the path between the node pairs in the supply chain network according to the path time consumption value, and sort the paths between the node pairs by heap sorting to obtain the average time consumption value and the maximum time consumption value; obtain the longest path length and the average path length of the supply chain network according to the average time consumption value and the maximum time consumption value; if the longest path length or the average path length is greater than a preset path length threshold, each path greater than the preset path length threshold is used as a critical path in the supply chain network; A key node determination module is used to obtain the node set of the key path according to the weighted directed graph, and obtain the predecessor node of each node of the node set through the adjacency matrix; according to the time characteristics of the predecessor node, the delivery cycle, production cycle and transportation time data of the predecessor node are accumulated by vector addition operation to obtain the weighted in-degree of each node; the sum of the duration of all successor nodes of each node is calculated to obtain the weighted out-degree of each node; according to the weighted in-degree of each node, the weighted out-degree is divided to obtain the time dependency of each node on the key path; according to the time dependency of each node, the time dependency is compared with the preset time dependency value, and the node with a time dependency value higher than the time dependency value is marked as the key node of the supply chain network; The key node optimization module is used to optimize the time of the key nodes according to the structural characteristics and time consumption characteristics of the key nodes to obtain an optimized supply chain network.

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