Unexpected accidents, such as earthquakes, snowstorms, and tsunamis, occur frequently at present. Emergency management is an important subject in both management science and social science, and has attracted increasing attentions. It is very important to determine emergency resources scheduling quickly and efficiently from multiple emergency logistics centers, as supply points, to multiple disaster affected points. In order to improve the efficiency of supplying organization and reduce casualties and economic losses, a decision support model whose target is to minimize the earliest rescuing time, the latest rescuing time and the number of supply activities simultaneously is first designed. Then, a multiagent genetic algorithm using natural coding is proposed to solve the designed problem. Computational experiments show that the proposed model is reasonable and the proposed algorithm is valid. At the same time, the scheduling found by the proposed algorithm can help decision-makers make a rational decision.
['Haolong Wu', 'Jing Liu']
A multi-agent genetic algorithm based on natural coding for emergency resources scheduling problems