Scheduling of rescue vehicles to forest fires via multi-objective Particle Swarm Optimization

It is complex and difficult to perform the rescue vehicle scheduling to handle forest fires in order to reduce the operational cost and improve the efficiency of fire-extinguishing services. A new research issue arises when a) decision-makers want to minimize the number of rescue vehicles while minimizing the rescue time; and b) decision-makers prefer to complete the fire-extinguishing task fast given limited vehicle resources. To do so, this work presents a novel multi-objective scheduling model to handle forest fires subject to limited rescue vehicle constraints. A fire spread speed factor is introduced into this model to better describe a practical forest fire. Also, a Multi-objective Particle Swarm Optimization (MPSO) algorithm is proposed to yield a set of Pareto solutions for this problem. This approach is applied to a real-world emergency scheduling problem of the forest fire in Mt. Daxing'anling, China. Both theoretical and simulation results demonstrate that the proposed approach is able to quickly produce Pareto solutions for decision makers.