Mean-risk stochastic integer programming approach for integrated fuel treatment and wildfire response planning with endogenous uncertainty

Abstract Two-stage mean-risk stochastic integer programming (MR-SIP) with endogenous uncertainty provides a powerful modeling tool for real-life decision-making problems, as it allows to capture here-and-now decisions that influence future outcomes. However, such problems are difficult to solve due to their nonconvexity and large-scale nature. We derive a decomposition method for this class of MR-SIP and apply it to a critical problem in wildfire management: optimal fuel treatment planning (FTP) under uncertainty. The uncertainty stems from fuels (vegetation), fire occurrence, and weather conditions. Fuel treatment methods such as prescribed burning, mechanical thinning, mowing, grazing, and chemical treatments are aimed at reducing hazardous fuels and thus, influence the uncertainty. In this work, we formulate a novel MR-SIP FTP model that integrates fuel treatment and firefighting resource deployment planning before fires happen, which are typically addressed in isolation rather than in an integrated manner. The new model uses the expected excess risk measure, which given a target level of wildfire damage cost, minimizes the mean excess above the target level. We parameterize the FTP model through standard wildfire behavior simulation software for generating fire scenarios and apply it to a real study area in West Texas, U.S.A. The results provide several practical insights for FTP decision-making. For example, the results reveal that when considering fuel treatment alone, treatment coverage is spread across the study area to high-risk subareas. However, integrating fuel treatment with resource deployment reduces coverage near operation bases, prioritizing high-risk subareas and reducing fire damage cost by 80% on average.