A Firefighting Resource Dispatch Problem Optimization Using Metaheuristics

Forest fires are a growing concern in our planet, as they have been increasing in frequency and severity over the last few decades. It is therefore essential to manage firefighting in order to prevent and reduce the damage caused to life, the economy and ecosystems. This work deals with the forest firefighting resource dispatch problem using metaheuristics. It is essential to know when and which suppression resources should be sent to extinguish the ignitions. Note that the faster the resources act, the smaller the burned area. An adapted genetic algorithm and differential evolution algorithm are used to solve a firefighting resource dispatch problem aiming to find the optimal allocation of resources to ignitions, and at what instant of time each resource is assigned to each ignition, in order to minimize the total burned area. The two metaheuristics are applied to a set of 60 randomly generated instances, comprising 5, 7 and 10 resources to extinguish 20 or 30 ignitions. A statistical analysis of the results is used to compare the efficiency of the metaheuristics, where the GA shows more competitive results.