Wildfire initial response planning using probabilistically constrained stochastic integer programming

Abstract

This paper presents a new methodology for making strategic dozer deployment plans for wildfire initial response planning for a given fire season. This approach combines a fire behaviour simulation, a wildfire risk model and a probabilistically constrained stochastic integer programming model, and takes into account the level of risk the decision-maker is willing to take when making deployment and dispatching plans. The new methodology was applied to Texas District 12, a Texas A&M Forest Service fire planning unit located in East Texas. This study demonstrates the effect of the decision-maker’s risk attitude level on deployment decisions in terms of the dozers positioned at each operations base, fires contained and their associated wildfire risk, and total containment cost. The results show that the total number of fires contained and their associated total expected cost increase when the tolerance towards risk decreases. Thus, more dozers are deployed to operations bases in areas with high wildfire risk and a high need for initial response.

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