Orateur
Description
Two-stage mean-risk stochastic integer programming (MR-SIP) with endogenous uncertainty involves here-and-now decisions that influence future outcomes and is very challenging to solve. We derive a decomposition method for this class of MR-SIP and apply it to an important problem in wildfire management, namely optimal fuel treatment planning (FTP) under uncertainty. The uncertainty stems from fuels (vegetation), fire occurrence, and fire behavior. Fuel treatment methods such as prescribed burning and mechanical thinning are aimed at reducing hazardous fuels and thus, influence the uncertainty. Consequently, FTP involving multiple treatment types and areas to treat to minimize wildfire risk is very challenging. In this work, we devise a novel MR-SIP FTP model that integrates fuel treatment and firefighting resource deployment planning before fires happen, which are typically done separately. 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 in generating scenarios and apply it in a case study to historical wildfire data for a study area in West Texas, U.S.A. The study provides several practical insights for FTP decision-making.
