A multi-objective evolutionary algorithm for emergency logistics scheduling in large-scale disaster relief

The emergency logistics scheduling (ELS) is to enable the dispatch of emergency supplies to the victims of disasters timely and effectively, which plays a crucial role in large-scale disaster relief. In this paper, we first design a new multi-objective model that considers both the total unsatisfied time and transportation cost for the ELS problem in large-scale disaster relief (ELSP-LDR), which is on the scenery of multi-disasters and multi-suppliers with several kinds of resources and vehicles. Then, a modified non-dominated sorting genetic algorithm II (mNSGA-II) is proposed to search for a variety of optimal emergency scheduling plans for decision-makers. With the intrinsic properties of ELSP-LDR in mind, we design three repair operators to generate improved feasible solutions. Compared with the original NSGA-II, a local search operator is also designed for mNSGA-II, which significantly improves the performance. We conduct two experiments (the case of Chi-Chi earthquake and Great Sichuan Earthquake) to validate the performance of the proposed algorithm.