| Title | Research on risk severity decision of cluster supply chain based on data flow fuzzy clustering |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | hong, Xue, zhifeng, Liao, yuan, Wang, ruidi, Xu, zhuoran, Xu |
| Conference Name | 2020 Chinese Control And Decision Conference (CCDC) |
| Keywords | analytic hierarchy process, Big Data, Cluster Supply Chain, Clustering algorithms, feature extraction, fuzzy clustering algorithm for data flow, high-dimensional risk assessment index, Indexes, Linear programming, Metrics, pubcrawl, risk management, Risk severity decision, supply chain risk assessment, Supply chains |
| Abstract | Based on the analysis of cluster supply chain risk characteristics, starting from the analysis of technical risk dimensions, information risk dimensions, human risk dimensions, and capital risk dimensions, a cluster supply chain risk severity assessment index system is designed. The fuzzy C-means clustering algorithm based on data flow is used to cluster each supply chain, analyze the risk severity of the supply chain, and evaluate the decision of the supply chain risk severity level based on the cluster weights and cluster center range. Based on the analytic hierarchy process, the risk severity of the entire clustered supply chain is made an early warning decision, and the clustered supply chain risk severity early warning level is obtained. The results of simulation experiments verify the feasibility of the decision method for cluster supply chain risk severity, and improve the theoretical support for cluster supply chain risk severity prediction. |
| DOI | 10.1109/CCDC49329.2020.9164270 |
| Citation Key | hong_research_2020 |
Research on risk severity decision of cluster supply chain based on data flow fuzzy clustering