Research on Optimization of Emergency Material Distribution in Two Stages during Sudden Public Health Emergencies

Following the outbreak of a sudden public health incident, the rational distribution of supplies is crucial for halting the spread of the event and ensuring the efficiency of the rescue efforts. This paper investigates the emergency supply distribution problem in the context of a sudden public health emergency. Initially, considering the differences among various demand points, an urgency evaluation index for demand points is constructed from four perspectives: personnel, environment, facilities, and supplies. Subsequently, a two-stage model for emergency supply allocation and route optimization is established based on four criteria: equity, time, efficiency, and economy. Furthermore, a Grey Wolf Optimizer is designed to solve the model, which is enhanced with strategies such as reverse learning, polynomial mutation, and simulated annealing concepts. Finally, the model and algorithm's effectiveness is validated using the COVID-19 epidemic in Wuhan as a simulation case study.