AI 中文总结
本研究针对医疗设施洪水风险，提出双目标两阶段随机优化模型，开发Benders分解与拉格朗日对偶方法，通过得州3752家机构案例验证，可在有限额外成本下平衡经济损失与社会脆弱地区服务中断影响。
AI 中文摘要
洪水会损坏医疗设施，中断当地护理能力，并引发代价高昂的患者疏散。应对这些风险需要在不确定性下进行长期的韧性投资。我们开发了一个双目标两阶段随机优化模型，共同确定永久性设施加固和情景依赖的疏散决策。第一个目标是最小化预期疏散、物理损坏和业务中断成本；第二个目标是最小化服务中断影响指数，该指数结合了服务损失的持续时间和规模，以及基于地点的社会脆弱性权重。我们开发了精确的Benders分解算法和可扩展的拉格朗日对偶方法，并定制了原始恢复策略，将两者嵌入到构建信息丰富的帕累托前沿的自适应过程中。针对得克萨斯州3752家医院和养老院的案例研究，在气候信息驱动的热带气旋洪水情景下评估了该框架。结果表明，仅最小化经济损失会系统性地减少对位于社会脆弱地区的设施的保护，而沿帕累托前沿适度移动可在有限的额外预期成本下大幅降低中断影响。精确分解算法可解决所有测试实例，包括大规模压力测试（其扩展公式存在内存限制），而拉格朗日方法能提供快得多的高质量解决方案。
英文摘要
Flooding can damage healthcare facilities, interrupt local care capacity, and trigger costly patient evacuations. Addressing these risks requires long-term resilience investments under uncertainty. We develop a bi-objective two-stage stochastic optimization model that jointly determines permanent facility hardening and scenario-dependent evacuation decisions. The first objective minimizes expected evacuation, physical damage, and business-interruption costs. The second minimizes a service-disruption impact index that combines the duration and scale of service loss with a place-based social-vulnerability weight. We develop an exact Benders decomposition and a scalable Lagrangian-dual method with tailored primal recovery, embedding both within an adaptive procedure for constructing informative Pareto frontiers. A case study of 3,752 hospitals and nursing homes in Texas evaluates the framework under climate-informed tropical-cyclone flood scenarios. The results show that minimizing economic losses alone can systematically allocate less protection to facilities located in socially vulnerable areas, while moderate movement along the Pareto frontier can substantially reduce disruption impacts at limited additional expected cost. The exact decomposition solves all tested instances, including larger stress tests for which the extensive formulation becomes memory-limited, while the Lagrangian method provides substantially faster high-quality solutions.
CommentsSubmitted manuscript with supplementary material. Data and code: https://doi.org/10.5281/zenodo.21510695
