在突发事件和大数据情景下,建立基于数据流模糊C均值聚类算法的集群式供应链应急物资需求重要度决策算法,有助于辨识集群式供应链子系统应急物资需求的重要程度.针对集群式供应链中各子供应链之间的耦合特性和预测指标的快速变化数据流特征,提出基于长短期记忆网络的集群式供应链应急物资需求动态预测算法,提取集群式供应链多个子系统应急物资需求参数的时序特征,动态地、分布地对互联大系统的应急物资需求不确定性进行系统辨识估计.仿真实验结果表明了基于长短期记忆网络的集群式供应链互联大系统应急物资需求动态预测算法的可行性和精确性.

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.

In order to solve the problem of optimal scheduling and reasonable allocation of limited materials in a short time after a natural disaster, a clustering supply chain emergency material distribution priority decision algorithm based on density clustering algorithm is proposed. Perform clustering as a factor indicator to determine the priority level of emergency material distribution in each supply chain in the clustered supply chain. Based on the material importance, timeliness, and gap index factors, a fuzzy C-means clustering algorithm for supply chain emergency material demand importance decision algorithm is proposed to classify a variety of emergency materials required in disaster areas when an emergency occurs., And decide the importance of each type of emergency supplies. The results of simulation experiments verify the feasibility of the emergency supply materials scheduling and importance decision-making method for the clustered supply chain. The decision results guarantee the optimal scheduling and allocation of limited supplies in the shortest possible time. And transportation programs provide theoretical support.

With changes in the social and economic environment, more and more small and medium-sized enterprises gather in the supply chain to form industrial clusters. How to customize reasonable prices and maximize the profit of products in cluster supply chain has become an important research topic. This paper is based on the clustering supply chain pricing strategy, mathematical modeling based on the pricing model of Mukhopadhyay economics, and then, the collaborative decision-making algorithm based on the double-layer adaptive genetic algorithm and particle swarm optimization algorithm is used to study the pricing scheme which can ensure the maximum profit of each supply chain and the total profit of the whole cluster supply chain. And study the impact on the optimization plan when consumer preferences change and the price elasticity of demand changes. It can be seen from the experimental results that this research has certain reference value for seeking optimal pricing for cluster supply chains.