Managing reliable emergency logistics for hazardous materials: A two-stage robust optimization approach

This paper investigates the impact of possible system disruptions on the performance of an emergency logistics system for hazardous materials (hazmats). More specifically, we first present a time-varying risk assessment method that takes into consideration the population dynamism. Then the two-stage robust optimization approach is employed to formulate two mixed-integer programming models, namely the basic and expanded unit commitment models, for managing a reliable emergency response system. The locations of emergency facilities are determined in the first stage, and recourse decisions are made in the second stage after the uncertain disruptions are realized. A column-and-constraint-generation algorithm is used to solve the proposed models exactly and tested on various sized random instances. A real-world case study with a series of numerical analyses reveals managerial insights that can be applied to facilitate more effective and efficient emergency responses.