Resource Dispatch Optimization for Firefighting Using a Differential Evolution Algorithm

The incidence of forest fires has shown an upward trend in recent years. This increase can be attributed to rising ambient temperatures and population growth, which act as the primary catalysts for these disasters. The application of optimization techniques has significantly contributed to addressing forest firefighting challenges, enabling improvements in the efficiency and promptness of firefighting operations. This study focuses on a specific resource dispatch problem to combat forest fires, which involves assigning 7 resources to extinguish 20 ignitions. The main objective is to minimize the total area burned by these ignitions in a minimum period of time. To solve this problem, the differential evolution algorithm adapted to this context was applied. Furthermore, a statistical analysis was performed to evaluate the performance of differential evolution when different selection and crossover operators are tested. The preliminary results show that the current-to-best selection and exponential crossover operators are the most suitable to solve the resource dispatch problem for forest firefighting.