Visible to the public Emergency Severity Assessment Method for Cluster Supply Chain Based on Cloud Fuzzy Clustering Algorithm

TitleEmergency Severity Assessment Method for Cluster Supply Chain Based on Cloud Fuzzy Clustering Algorithm
Publication TypeConference Paper
Year of Publication2019
AuthorsXue, Hong, Wang, Jingxuan, Zhang, Miao, Wu, Yue
Conference Name2019 Chinese Control Conference (CCC)
ISBN Number978-9-8815-6397-2
Keywordscloud computing, cloud fuzzy clustering algorithm, cloud model processing, cluster centers, cluster microcluster weights, Cluster Supply Chain, cluster supply chain emergency, Collaboration, composability, composite uncertainty characteristics, data stream fuzzy clustering algorithm, emergency management, Emergency Risk, emergency severity assessment indexes, emergency severity assessment method, fuzzy set theory, high-dimensional data stream characteristics, Human Behavior, human factors, Metrics, multidata fusion method, optimisation, pattern clustering, policy-based governance, pubcrawl, resilience, Resiliency, Risk Severity Assessment, Scalability, sensor fusion, sliding window model, summary cloud model generation algorithm, Sun mary Cloud Model Generation Algorithm, supply chain risk assessment, synopsis data, time attenuation model, Voltage control

Aiming at the composite uncertainty characteristics and high-dimensional data stream characteristics of the evaluation index with both ambiguity and randomness, this paper proposes a emergency severity assessment method for cluster supply chain based on cloud fuzzy clustering algorithm. The summary cloud model generation algorithm is created. And the multi-data fusion method is applied to the cloud model processing of the evaluation indexes for high-dimensional data stream with ambiguity and randomness. The synopsis data of the emergency severity assessment indexes are extracted. Based on time attenuation model and sliding window model, the data stream fuzzy clustering algorithm for emergency severity assessment is established. The evaluation results are rationally optimized according to the generalized Euclidean distances of the cluster centers and cluster microcluster weights, and the severity grade of cluster supply chain emergency is dynamically evaluated. The experimental results show that the proposed algorithm improves the clustering accuracy and reduces the operation time, as well as can provide more accurate theoretical support for the early warning decision of cluster supply chain emergency.

Citation Keyxue_emergency_2019