Using auction-based task allocation scheme for simulation optimization of search and rescue in disaster relief.

In order to improve the efficiency of search and rescue (SAR) in disaster relief, we optimize the SAR through agent-based simulation in this paper. The Truncated Lévy walks model is adopted to simulate rescuers’ search behaviors, and we improve it to fit the disaster environment. An auction-based task allocation scheme is used to develop a cooperative rescue plan. To verify the effectiveness of the proposed scheme, we illustrate it with the case of landslide disaster relief, and simulate it in three scenarios ("fatal", "serious" and "normal"). We compare it with non-cooperative rescue plan and the rescue plan based on well-known F-Max-Sum. The simulation results indicate that the cooperative rescue plan could improve the rescue efficiency significantly, and it performs somewhat better than the F-Max-Sum-based approach in regard to some indicators. Furthermore, its low complexity has made it more appropriate for the cooperation among rescue teams than F-Max-Sum. The robustness analysis shows that search radius can affect the rescue efficiency significantly, while the scope of cooperation has little effect on the rescue efficiency. The sensitivity analysis shows that the two parameters, the time limit for completing rescue operations in one buried site and the maximum turning angle for next step, both have great influence on rescue efficiency, and there exist optimal value for both of them in view of rescue efficiency.