A Stochastic Programming Model for Casualty Response Planning During Catastrophic Health Events
构建了一个三阶段随机规划模型，用于在灾难性健康事件中选址替代护理设施并分配伤员，整合了伤员分类和自疏散者流动，并提出了基于Benders分解的快速求解算法。
Catastrophic health events are natural or man-made incidents that create casualties in numbers that overwhelm the response capabilities of healthcare systems. Proper response planning for such events requires community-based surge solutions such as the location of alternative care facilities and ways to improve coordination by considering triage and the movement of self-evacuees. In this paper, we construct a three-stage stochastic programming model to locate alternative care facilities and allocate casualties in response to catastrophic health events. Our model integrates casualty triage and the movement of self-evacuees in a systemic response framework that treats uncertainties involved in such large-scale events as probabilistically distributed scenarios. Solution times being instrumental to the practicality of the model, we propose an algorithm, based on Benders decomposition, to generate good solutions fast. We derive new valid inequalities, which we add to the Benders decomposition master problem to reduce the number of weak feasibility cuts generated. Because our algorithm can also be ineffective if the number of scenarios is large, we propose a two-stage approximation model that attempts to guess good third-stage solutions without third-stage decision variables and constraints. Our model, algorithm, and two-stage approximation are implemented in the case study of an earthquake situation in California based on the realistic ShakeOut Scenario data. The online appendix is available at https://doi.org/10.1287/trsc.2017.0777 .