The results in different settings.

Post-earthquake emergency logistics faces significant challenges such as limited resources, uncertain casualty numbers, and time constraints. Developing a scientific and efficient rescue plan is crucial. One of the key issues is integrating facility location and casualty allocation in emergency medical services, an area rarely explored in existing research. This study proposes a robust optimization model to optimize the location of medical facilities and the transfer of casualties within a three-level rescue chain consisting of disaster areas, temporary hospitals, and general hospitals. The model accounts for limited medical resources, casualty classification, and uncertainty in casualty numbers. The Trauma Index Score (TIS) method is used to classify casualties into two groups, and the dynamic changes in their injuries after treatment at temporary hospitals are considered. The objective is to minimize the total TIS of all casualties. A robust optimization approach is applied to derive the corresponding robust model, and its validity is verified through case studies based on the Lushan earthquake. The findings show that data variability and the uncertainty budget play a critical role in determining hospital locations and casualty transportation plans. Temporary hospital capacity significantly influences the objective function more than general hospitals. As the problem size increases, the robust optimization model performs better than the deterministic model. Furthermore, uncertainty in casualty numbers has a more significant impact on serious casualties than moderate casualties. To enhance the model’s applicability, it is extended into a two-stage dynamic location-allocation model to better address the complexity of post-disaster scenarios.震后应急物流面临资源有限、伤亡人数不确定、时间紧迫等多重严峻挑战，制定科学高效的救援方案至关重要。其中核心难题之一是在急诊医疗服务场景中整合医疗设施选址与伤员分配工作，而该领域在现有研究中鲜有涉及。本研究针对由灾区、临时医院与综合医院构成的三级救援链，提出鲁棒优化模型，用于优化医疗设施选址与伤员转运方案。该模型考量医疗资源受限、伤员分级以及伤亡人数不确定性等要素：采用创伤指数评分（Trauma Index Score, TIS）法将伤员划分为两类，并纳入伤员在临时医院接受救治后的伤情动态变化，优化目标为最小化全体伤员的总创伤指数评分。本研究通过鲁棒优化方法推导得到对应鲁棒模型，并基于芦山地震的案例研究验证了模型有效性。研究结果表明，数据波动性与不确定性预算在医院选址及伤员转运方案的制定中发挥关键作用；相较于综合医院，临时医院的床位容量对目标函数的影响更为显著。随着问题规模扩大，鲁棒优化模型的表现优于确定性模型；此外，伤亡人数的不确定性对重伤伤员的影响较中度伤员更为显著。为提升模型适用性，本研究将其拓展为两阶段动态选址-分配模型，以更好地适配灾后场景的复杂性。