Emergency logistics scheduling in disaster relief based on a multi-agent genetic algorithm

The disaster emergency relief plays a vital role in reducing casualties and economic losses. Emergency logistics scheduling (ELS) aims at dispatching emergency resources to the victims of disasters, which is an important event of disaster relief. In this paper, a model for ELS in disaster relief is built that includes several suppliers with a variety of resources, several kinds of vehicles, and multiple disasters. To ensure timely and effective dispatching, an objective is first designed to minimize the completion time of scheduling and the total unsatisfied time of the whole relief. Then, a multi-agent genetic algorithm (MAGA) is proposed to solve this problem. Four evolutionary operators, namely, the crossover, mutation, self-learning and the effective neighborhood competition operators are designed for agents. The proposed algorithm is applied to the case of Chi-Chi earthquake in Taiwan, and the experimental results show that the performance of this algorithm is better than that of genetic algorithm and memetic algorithm.