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Robust Optimization for Vehicle Routing Problem Under Uncertainty in Disaster Response

机译：不确定响应下车辆路径问题的鲁棒优化

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The robust optimization problem for emergency logistics in response to disaster is studied in this paper. An improved mixed integer programming model for capacitated vehicle routing problem (CVRP) is developed, which minimizes the total travel cost and unmet demands. Due to the suddenness and unpredictability of disasters, precise information is usually unavailable especially in the early period after the occurrence. Hence the concept of robustness is adopted to handle the uncertainty. The information about demands and travel costs is represented in intervals, which can be estimated based on the historical data. Numerical experiments are conducted and the computational results reveal that the results of the robust optimization model can better balance the total cost and the risk due to the uncertainty.
机译：研究了应急物流响应灾害的鲁棒优化问题。开发了一种改进的混合整数规划模型，用于求解有能力的车辆路径问题（CVRP），该模型将总行驶成本和未满足的需求降到最低。由于灾难的突发性和不可预测性，通常无法获得准确的信息，尤其是在灾难发生后的早期。因此，采用鲁棒性的概念来处理不确定性。有关需求和差旅成本的信息以时间间隔表示，可以根据历史数据进行估算。进行了数值实验，计算结果表明，鲁棒优化模型的结果可以较好地平衡总成本和不确定性带来的风险。

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