Dynamic adjustment method for optimizing epidemic-logistics network based on data-driven

Abstract

To improve the effect of emergency response in epidemic controlling, the corresponding logistics network should be adjusted dynamically because an unexpected epidemic outbreak has several typical unstructured features, including the fuzzy boundary and time-varying decision scenarios. In this paper, an innovative decision framework for optimizing the epidemic-logistics network based on data-driven is proposed. The whole emergency response time is divided to be multiple and continuous cycles. Emergency response process in each decision-making cycle involves four steps, which are epidemic dynamics analysis, emergency distribution network design, data collection, and parameters adjustment. Under this new decision framework, the entire emergency response process can be converted to an interactive evolution process of data learning and resource optimization. Numerical tests demonstrate that the proposed new decision framework can provide several real-time and effective policies for controlling an unexpected epidemic outbreak. Moreover, it also provides useful decision-making reference for other emergencies.