Investigation of risk propagation and control in emergency response logistics networks: A cellular automata based approach

Abstract

Emergency logistics is crucial for responding to natural disasters and public health emergencies. In emergency logistics systems, risk propagation must be effectively controlled and mitigated, and otherwise it may lead to serious consequences. This study, for the first time, investigates the risk propagation of emergency logistics networks and their protection strategies from a dynamic perspective using the method of cellular automata. The purpose is to reveal the risk propagation mechanism of emergency logistics networks and its influencing factors, which provides a basis for improving the risk resistance of emergency logistics networks. Specifically, this paper constructs an emergency logistics network based on the weighted scale-free algorithm and establishes a risk propagation model by using metric automata. Also, this paper carries out a comprehensive numerical investigation on risk propagation in the emergency logistics network by adjusting the model parameters and adopting different protection strategies. The results of this research indicate that (1) the speed and range of risk propagation are not sensitive to the network size, while the reduction of the network size leads to an increase in the volatility and a decrease in the stability of the propagation; (2) the state characteristics of the network nodes are more important than the structural characteristics of the network for controlling the risk of node vulnerability; (3) increasing the level of risk recovery is more effective than reducing risk infection in controlling the scope of risk propagation throughout the network; (4) finally, the targeted protection strategy is more effective than the random protection strategy. The findings are potentially impactful in that they reveal the nature of risk propagation in emergency logistics networks, further expand methodological research of risk propagation, and provide a solid foundation for effective risk management and control.