AI 中文总结
针对野火脆弱人群受支持疏散问题，开发两阶段随机优化模型，采用含组合Benders割等的基于逻辑的Benders分解方法，经实际数据验证可提升疏散效率。
AI 中文摘要
本研究针对野火期间脆弱人群（如医院患者和长期护理居民）受支持疏散这一关键但研究不足的领域，开发了一个两阶段随机优化模型，用于在严格时间窗下优化设施选址、车队规模和车辆路径规划。为克服该问题的NP难复杂性，作者提出了一种创新的解决方案方法，利用基于逻辑的Benders分解，包含组合Benders割和基于逻辑的不等式。通过在科罗拉多州罗克斯伯勒公园开展的社区野火演练的大量数值实验和真实世界数据表明，与其他政策相比，所提方法能产生高质量的解决方案，显著改善避难所布置、车辆利用率和整体疏散效率。
英文摘要
This study addresses the critical yet under researched area of supported evacuation for vulnerable populations during wildfires, such as hospital patients and long term care residents, by developing a two stage stochastic optimization model that optimizes facility location, fleet sizing, and vehicle routing under strict time windows. To overcome the problem NP hard complexity, the authors propose an innovative solution methodology leveraging Logic Based Benders Decomposition, featuring Combinatorial Benders Cuts and logic based inequalities. Extensive numerical experiments and real world data from a community wildfire drill in Roxborough Park, Colorado, demonstrate that the proposed approach yields high quality solutions, significantly improving shelter placement, vehicle utilization, and overall evacuation efficiency compared to alternative policies.
Comments33 pages, 8 figures
