基于深度学习的集群式供应链应急物资需求预测研究

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。

Demand forecasting of cluster supply chain emergency materials based on deep learning

In the case of emergencies and big data, the importance decision algorithm of emergency material demand based on data stream fuzzy C-means clustering algorithm is established to identify the importance of emergency material demand for the cluster supply chain subsystem. For the coupling cha- racteristics between the cluster supply chain subsystems and fast changing data stream characteristics of prediction indexes, the dynamic forecasting algorithm of emergency material demand based on the long short-term memory network is proposed for cluster supply chain to extract the time series characteristics of emergency material demand parameters from multiple subsystems of cluster supply chain. The system identification and estimation for the uncertainties of emergency material demand of the interconnected large-scale system are carried out dynamically. The simulation results prove the feasibility and accuracy of the dynamic prediction algorithm for emergency material demand based on the long short-term memory network in cluster supply chain.