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<p>Wildfires have become increasingly frequent and severe, posing escalating environmental, social, and economic risks worldwide. Their complex, dynamic, and uncertain nature demands analytical decision-support frameworks that go beyond traditional heuristic or experience-based approaches. Operations Research provides a rigorous foundation for modeling these challenges through optimization, simulation, and data-driven methods that can enhance prevention, preparedness, response, and recovery planning. This paper presents a systematic review of OR-based wildfire management research published between 2000 and 2024. Following the PRISMA framework, 177 peer-reviewed studies were identified and analyzed based on problem type, decision context, and treatment of uncertainty. The review classifies the literature into three key methodological paradigms: (i) simulation and hybrid simulation-optimization models that evaluate fire spread and suppression effectiveness under uncertainty; (ii) stochastic and robust optimization frameworks that capture probabilistic and worst-case scenarios in resource allocation and risk mitigation; and (iii) decomposition and metaheuristic algorithms that address the computational complexity of large-scale, multi-period, and spatially detailed problems. Despite substantial methodological progress, major challenges persist. Data availability and quality remain constrained by the high cost of LiDAR acquisition, incomplete satellite coverage, and insufficient real-time field data. Many models oversimplify fire dynamics or lack integration with operational decision systems. These limitations underscore the need for analytical frameworks that are scalable, interpretable, and integrative. Findings demonstrate that OR has become an essential analytical pillar in wildfire management, transforming how uncertainty, complexity, and interdependent decisions are modeled and providing a pathway toward more resilient, data-informed, and operationally deployable wildfire strategies.</p>

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Wildfire Management: A Systematic Review of Optimization Under Uncertainty and Complexity

  • 期刊论文

  • 发表日期：

    2026年1月26日

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  • Paria Rostamian,
  • Amin Ahmadi Digehsara,
  • Kibele Sebnem Yildirim,
  • Amir Ardestani-Jaafari

摘要

Wildfires have become increasingly frequent and severe, posing escalating environmental, social, and economic risks worldwide. Their complex, dynamic, and uncertain nature demands analytical decision-support frameworks that go beyond traditional heuristic or experience-based approaches. Operations Research provides a rigorous foundation for modeling these challenges through optimization, simulation, and data-driven methods that can enhance prevention, preparedness, response, and recovery planning. This paper presents a systematic review of OR-based wildfire management research published between 2000 and 2024. Following the PRISMA framework, 177 peer-reviewed studies were identified and analyzed based on problem type, decision context, and treatment of uncertainty. The review classifies the literature into three key methodological paradigms: (i) simulation and hybrid simulation-optimization models that evaluate fire spread and suppression effectiveness under uncertainty; (ii) stochastic and robust optimization frameworks that capture probabilistic and worst-case scenarios in resource allocation and risk mitigation; and (iii) decomposition and metaheuristic algorithms that address the computational complexity of large-scale, multi-period, and spatially detailed problems. Despite substantial methodological progress, major challenges persist. Data availability and quality remain constrained by the high cost of LiDAR acquisition, incomplete satellite coverage, and insufficient real-time field data. Many models oversimplify fire dynamics or lack integration with operational decision systems. These limitations underscore the need for analytical frameworks that are scalable, interpretable, and integrative. Findings demonstrate that OR has become an essential analytical pillar in wildfire management, transforming how uncertainty, complexity, and interdependent decisions are modeled and providing a pathway toward more resilient, data-informed, and operationally deployable wildfire strategies.