A decomposition approach for the stochastic asset protection problem

Abstract

The deterministic Asset Protection Problem (APP) involves deploying firefighting resources of various capabilities to service as many community assets as possible within time windows determined by an advancing wildfire. A common situation arising during these situations is a wind change. Forecasts of changes in wind velocity (i.e. direction and speed) are reasonably accurate but there is some uncertainty around the time of a wind change. The timing has implications for which areas, and hence which assets, will be impacted by the wildfire. This presents a difficult problem for an Incident Management Team (IMT) operating under severe time pressure as the wildfire sweeps across the landscape. In this study, we extend the spatial decomposition-based math-heuristic originally developed for the deterministic APP to handle large real life sized stochastic APPs in operational time. This is achieved by regarding the deterministic and stochastic components as a coupled system. A two-stage stochastic programming (TSSP) model is thus only used for a smaller sub-problem around the uncertainty. We found this approach outperformed other methods when tested on a new set of benchmarks. More importantly, the accuracy and solution times make the method suitable for operational purposes.