Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

• GSMOGA combines the advantages of the greedy search method and MOGA.
• GSMOGA is able to quickly generate a number of feasible solutions.
• GSMOGA arranges schedules (feasible solutions) based on multi-depot and multidemand.
• GSMOGA reveals what resources are required and acquired at demand and supply point.
• GSMOGA is capable of arranging routing schedules for each form of transport.

To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi–Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in ‘time to delivery’ by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.