Intense wildfire seasons require critical prioritization decisions to allocate scarce suppression resources over a dispersed geographical area. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression. The problem features a discrete resource-allocation structure with endogenous wildfire demand and non-linear wildfire dynamics. We formulate an integer optimization model with crew assignments on a time-space-rest network, wildfire dynamics on a time-state network, and linking constraints between them. We develop a two-sided 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 propose a data-driven double machine learning approach to estimate wildfire spread as a function of covariate information and suppression efforts, mitigating observed confounding between historical crew assignments and wildfire growth. Extensive computational experiments show that the optimization algorithm scales to otherwise intractable real-world instances; and that the methodology can enhance suppression effectiveness in practice, resulting in significant reductions in area burned over a wildfire season and guiding resource sharing across wildfire jurisdictions.

翻译：日益严峻的野火季节要求在广阔的地理区域内做出关键优先级决策，以有效分配稀缺的扑救资源。本文提出了一种预测性与规范性相结合的方法，用于联合优化扑救队伍分配与野火扑救行动。该问题具有离散资源配置结构，并涉及内生性野火需求和非线性野火动态。我们构建了一个整数优化模型，包含基于时空约束网络的扑救队伍分配、基于时间状态网络的野火动态以及两者之间的耦合约束。在此基础上，我们开发了一种双边分支定价切割算法，其核心包括：（i）一种双边列生成方案，可迭代生成火势扑救计划与扑救队伍路径；（ii）利用耦合约束中背包结构的新型切割不等式族；（iii）适用于非线性野火动态的新型分支规则。此外，我们提出了一种数据驱动的双重机器学习方法，用于估算野火蔓延与协变量信息及扑救努力之间的函数关系，从而减轻历史扑救队伍分配与野火增长之间存在的观测混杂效应。大量计算实验表明，该优化算法可扩展至原本难以求解的现实实例；在实际应用中，该方法能有效提升扑救效率，显著减少整个野火季节的过火面积，并指导跨辖区的资源调配。