Two-Stage Stochastic Programming Model for Improving Transportation Network Resilience of Relief Supplies in Sequential Hazards Scenario

Abstract

Cascading failure of road transport networks caused by complex sequential natural hazards adversely affects the use of pre-positioning relief supplies. Therefore, it is vital to improve the transportation network resilience of relief supplies by using an optimization model. The purpose of this paper is to pre-position and distribute relief supplies in uncertain scenarios of sequential hazards. A two-stage stochastic programming model to maximize the total resilience is proposed to provide an optimal plan against the uncertain impact of sequential natural hazards. The combined impact of the Jiuzhaigou 7.0 magnitude earthquake and its associated landslide is a prototype disaster scenario for the implementation of the method proposed in this paper. The model is solved by a neighborhood search-based genetic algorithm (NS-based GA), which has both the global search capability of a genetic algorithm and the local search capability of a large-scale neighborhood search algorithm, can improve the solution finding capability. A case study focusing on finding the optimal solution for the pre-position and distribution of relief supplies in the sequential hazard of Jiuzhaigou earthquake is conducted to illustrate the validity of the proposed model.