This paper proposes a scenario-based three-stage hybrid robust and stochastic model that optimally designs the response network and distributes casualties effectively under uncertain combinational scenarios of primary and secondary disasters. Following the stochastic severity of combinational disasters, the robust counterparts are derived against the ambiguous uncertainty of evacuee scales and transportation time, respectively. A customized progressive hedging algorithm based on the augmented Lagrangian relaxation is developed to solve the problem. We decompose the problem based on the scenario and iteratively solve the adaptively penalized sub-problems with decision variables independent of stages. The results of an illustrative example show that incorporating secondary disaster scenarios can contribute to improving relief coverage. The proposed algorithm is competitive with some benchmarks.

本文提出了一种基于场景的三阶段混合鲁棒和随机模型，该模型在不确定的一次和二次灾害组合场景下，优化设计了响应网络并有效地分配了人员伤亡。伴随着组合灾害的随机严重性，分别针对疏散规模和运输时间的不确定性得出了强有力的对应物。提出了一种基于增强拉格朗日松弛的定制渐进式套期保值算法。我们基于场景分解问题，并迭代解决具有独立于阶段的决策变量的自适应惩罚子问题。说明性示例的结果表明，将次生灾害情景纳入其中可以有助于提高救灾覆盖率。