Abstract
Abstract
Due to climate change, prolonged droughts, and extreme weather, wildfires are becoming more frequent and are reaching the level of natural disasters. Under these new conditions, it is critical to develop rapid and effective fire containment strategies.This paper presents an online optimization framework for wildfire containment during concurrent multiple wildfire incidents. The method integrates wildfire damage prediction with a mixed-integer linear programming (MILP) model to optimize the allocation and scheduling of firefighting resources to minimize total losses. Within the optimization framework, operational limits such as resource availability, travel time, and containment effectiveness are incorporated into the decision-making process as the wildfire evolves.The proposed method reduced total losses by as much as 45.1% in some cases compared with a traditional priority-based resource dispatching method. Computational tests on various wildfire scenarios also show that adding additional resources may not linearly translate into loss reduction.Overall, the framework offers an effective and computationally feasible method for planning dynamic wildfire containment, allowing for optimized resource allocation under changing fire conditions.The findings emphasize the value of using predictive simulation and optimization techniques to support effective wildfire emergency management.