Post-Earthquake Emergency Logistics Location-Routing Optimization Considering Vehicle Three-Dimensional Loading Constraints

Abstract

An efficient humanitarian emergency logistics network is vital in responding to earthquake disasters. However, the asymmetric information inherent in the location and distribution stages can complicate the humanitarian emergency logistics network designing process, resulting in an asymmetric optimization problem. This paper addresses a multi-objective humanitarian emergency logistics network design problem during the earthquake response phase. The objective is to reduce societal expenses (e.g., logistical and deprivation costs) and mitigate risk to the logistics network by identifying ideal sites for distribution hubs, optimal emergency material distribution strategies, and precise material loading plans. The proposed model takes into account various constraint types, such as 3D loading limitations for relief materials, interruptions in distribution hubs, distribution centers’ capacity, transport vehicles’ capacity, and specific time windows for demand points. First, a multi-objective mixed-integer programming model is established to solve the problem. Uncertainty is modeled using a scenario-based probability approach. Second, a multi-objective genetic algorithm based on adaptive large neighborhood search (MOGA-ALNS) is designed to further optimize the solutions obtained from the evolutionary process using an adaptive large neighborhood search algorithm. Furthermore, the MOGA-ALNS integrates a simulated annealing process in the neighborhood search stage to inhibit the algorithm from reaching local optimums. Ultimately, the MOGA-ALNS is compared to three additional multi-objective optimization algorithms. The comprehensive analysis and discussion conducted unequivocally validate the competitiveness and efficacy of the proposed approach.