Robust stochastic vehicle routing and scheduling for bushfire emergency evacuation: An Australian case study
Robust stochastic vehicle routing and scheduling for bushfire emergency evacuation: An Australian case study
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DOI：
10.1016/j.tra.2017.04.036
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发表时间：
2017-10
影响因子：
6.4
通讯作者：
S. Shahparvari;B. Abbasi
中科院分区：
文献类型：
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作者：
S. Shahparvari;B. Abbasi
本文提出了一种随机建模方法作为疏散决策支持系统，在疏散人口、时间窗口和林火传播的不确定性下，确定疏散所需的车辆、调度和路线。拟议的模型还考虑了道路的可用性和中断。针对车辆路径问题的复杂性，提出了一种贪婪的求解方法。此外，通过与所设计的遗传算法在不同数值算例上的比较，对所提出的解的有效性进行了评估。然后将该模型应用于澳大利亚维多利亚州2009年黑色星期六森林大火的真实案例研究。利用黑色星期六的历史数据，生成了几种看似合理的疏散情景。采用频数法对结果进行分析，确定最优疏散方案。结果显示，即使在艰难的时间窗口和最大人口数量的情况下，在黑色星期六疏散晚些时候的疏散人员也是可能的。
This study proposes a stochastic modeling approach as an evacuation decision support system to determine the required vehicles, scheduling and routes under uncertainties in evacuee population, time windows and bushfire propagation. The proposed model also considers road availability and disruptions. A greedy solution method is developed to cope with the complex nature of vehicle routing problem. Furthermore, the effectiveness of the proposed solution is evaluated by comparison with a designed genetic algorithm on sets of various numerical examples. The model is then applied on the real case study of the 2009 Black Saturday bushfires in Victoria, Australia. Several plausible evacuation scenarios are generated, utilizing the historical data of Black Saturday. The results are analyzed using the frequency approach to determine the optimal evacuation plan. The results show that it would have been possible to evacuate the late evacuees on Black Saturday, even within hard time windows and a maximum population.