Managing hazmat emergency logistics with random travel time: a distributionally robust approach

Abstract

In the field of emergency logistics involving hazardous materials (hazmat), the optimization of emergency facility locations and risk mitigation is paramount. Prior research primarily focused on emergency planning with deterministic travel times. However, real-world emergency responses encounter diverse factors leading to uncertainties in travel duration. This study addresses the hazmat emergency facility location and allocation problem by considering stochastic emergency response times. The proposed distributionally robust optimization model aims to minimize emergency facility construction costs while concurrently mitigating potential system risk under the worst-case distribution of response time within an ambiguity set. Given limited distribution data derived from historical records, two methodologies are employed to convert this data into tractable ambiguity sets. Experimental assessments conducted using a hypothetical and a real-world case study in China showcase the superior efficacy and efficiency of the proposed approach. Furthermore, sensitivity analyses of parameters shed light on the various factors influencing the system, illustrating the interplay between cost minimization and risk mitigation objectives, and offering optimal solutions for different parameter configurations. These findings yield invaluable insights for decision-makers involved in hazmat emergency response operations.