Simulation Optimization of Search and Rescue in Disaster Relief Based on Distributed Auction Mechanism.

In this paper, we optimize the search and rescue (SAR) in disaster relief through agent-based simulation. We simulate rescue teams' search behaviors with the improved Truncated Levy walks. Then we propose a cooperative rescue plan based on a distributed auction mechanism, and illustrate it with the case of landslide disaster relief. The simulation is conducted in three scenarios, including "fatal", "serious" and "normal". Compared with the non-cooperative rescue plan, the proposed rescue plan in this paper would increase victims' relative survival probability by 7-15%, increase the ratio of survivors getting rescued by 5.3-12.9%, and decrease the average elapsed time for one site getting rescued by 16.6-21.6%. The robustness analysis shows that search radius can affect the rescue efficiency significantly, while the scope of cooperation cannot. 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 a great influence on rescue efficiency, and there exists optimal value for both of them in view of rescue efficiency.