Wildfire Suppression: Complexity, Models, and Instances

Abstract:Wildfires cause major losses worldwide, and the frequency of fire-weather conditions is likely to increase in many regions. We study the allocation of suppression resources over time on a graph-based representation of a landscape to slow down fire propagation. Our contributions are theoretical and methodological. First, we prove strong NP-completeness on planar graphs for this problem and two related variants, and on full weighted directed grids for two of the three problems. We also show that this problem remains strongly NP-complete when all resources are released simultaneously. Second, we propose a new mixed-integer programming (MIP) formulation that obtains state-of-the-art results, showing that MIP is a competitive approach contrary to earlier findings. Third, showing that existing benchmarks lack realism and difficulty, we introduce a physics-grounded instance generator based on Rothermel's surface fire spread model. We use these diverse instances to benchmark the literature, identifying the specific conditions where each algorithm succeeds or fails.

Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI)

arXiv:2603.29865 [cs.CE]

(or arXiv:2603.29865v2 [cs.CE] for this version)

https://doi.org/10.48550/arXiv.2603.29865 Focus to learn more arXiv-issued DOI via DataCite

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