Abstract:
Intense wildfire seasons require critical prioritization decisions to suppress wildfires over a disperse geographic area with limited resources. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression. The problem features a combinatorial resource allocation structure with endogenous wildfire demand and non-linear wildfire dynamics. We formulate an integer optimization model in a time-space-rest network representation of crew assignments and a time-state network representation of wildfire dynamics, with linking constraints to ensure consistency between both. We develop a branch-and-price-and-cut algorithm based on: (i) a two-sided column generation scheme that generates fire suppression plans and crew routes iteratively; (ii) a new family of cuts exploiting the knapsack structure of the linking constraints; and (iii) novel branching rules to accommodate non-linear wildfire dynamics. We also develop a data-driven approach based on double machine learning and causal forests to estimate wildfire spread as a function of covariate information and suppression efforts, while controlling for the endogeneity of treatment and outcome variables. Extensive computational experiments show that the optimization algorithm scales to practical and otherwise intractable instances; and that the methodology can enhance suppression effectiveness, resulting in a significant reduction in area burned over a wildfire season.

About the Speaker:
Prof. Alexandre Jacquillat is the Maurice F. Strong Career Development Professor and an Associate Professor of Operations Research and Statistics at the MIT Sloan School of Management. His research develops algorithms for large-scale discrete optimization and optimization under uncertainty, with applications to urban mobility, sustainability, health care and other social good operations. Prof. Jacquillat is the recipient of several awards, including the INFORMS Dantzig Dissertation Award, the Best Paper Prize from INFORMS Transportation Science and Logistics (twice), the Harvey Greenberg Research Award from INFORMS Computing, the Pierskalla Best Paper Award from INFORMS Health Applications, and the Best Paper Award from INFORMS Data Mining and Decision Analytics. He received a Master of Science in Applied Mathematics from the Ecole Polytechnique and PhD in Engineering Systems from MIT.

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