Robustifying humanitarian relief systems against travel time uncertainty
研究了灾害管理中多中心多区域的最后一公里配送问题，提出机会约束模型和分布鲁棒优化方法，同时确定应急物流中心选择、物资运输量、车辆分配与路径，以及物资公平分配，以玉树地震案例验证模型能降低成本并提高可靠性。
Abstract We consider a class of last mile distribution problems with multicenters and multiareas in disaster management, where the travel time for every pair of center‐area is uncertain. We propose a chance‐constrained model to handle this problem, which simultaneously determines the selection of emergency logistics centers (ELCs), the amount of relief transported to these ELCs, the allocation and routing of vehicles, and the equitable distribution of emergency materials from the ELCs to the disaster areas. We develop a distributionally robust optimization (DRO) model to reformulate the chance‐constraint model as a second‐order cone programming, which the proposed algorithm can solve efficiently. We present a case study of the Yushu earthquake in the Qinghai Province of the People's Republic of China to demonstrate the performance of our proposed model and method. The results reveal that (1) the DRO method outperforms the scenario‐based model in reducing cost and improving reliability, (2) deploying a large number of vehicles in the emergency centers near disaster areas can reduce cost, and (3) our proposed model evenly distributes vehicles on paths to curtail the rescue time.