Discrete Event Simulation in a Firefighting Resource Scheduling Problem

Every year the world is faced with a natural and human-caused catastrophe: forest fires. Firefighting depends on quick and efficient decision-making. Delays can lead to large areas being devastated and many lives at risk. On the other hand, when there are simultaneous ignitions, prioritization criteria must be used to minimize this phenomenon’s impacts. Decision-making in this context of urgency and effectiveness has led to the use of Operations Research techniques, both in the optimization process and in the simulation of possible scenarios. In this work, the Genetic Algorithm (GA) is used to optimize the firefighting resource scheduling problem. A case study is addressed, whose main objective is to determine the scheduling of two available resources to extinguish ten fire ignitions while minimizing the total burned area (TBA). Two distinct GA population sizes are used in the optimization procedure in order to assess the one that produces better results. Then, a Discrete Event Simulation model is generated in FlexSim software, considering three scenarios. The first one is based on the best solution obtained by the GA, and the other two adding uncertainty to the processing and travel time. The simulation results validated the GA’s optimal scheduling and allowed evaluating the impact of the TBA when considering uncertainty.