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Evacuating People with Mobility-Challenges in a Short-Notice Disaster

机译:在短暂的灾难中疏散行动不便的人

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In past disasters, arrangements have been made to evacuate people without their own transportation, requiring them to gather at select locations to be evacuated. Unfortunately, this type of plan does not help those people who are unable to move themselves to the designated meeting locations. In the United States, according to the Post-Katrina Emergency Management Reform Act of 2006, state or local governments have the responsibility to coordinate evacuation plans for all populations. These include those with disabilities. However, few, if any, have plans in place for those who are mobility-challenged. The problem of evacuating mobility-challenged people from their individual locations in a short-notice disaster is a challenging combinatorial optimization problem. In order to develop the model and select a solution approach, we surveyed related literature. Based on our review, we formulate the problem and develop an Ant Colony Optimization (ACO) algorithm to solve it. We then test two different versions of the ACO algorithm on five stylized datasets with several different parameter settings.
机译:在过去的灾难中,已经安排了无人运输的人员撤离,要求他们聚集在要撤离的特定地点。不幸的是,这种计划无法帮助那些无法将自己转移到指定会议地点的人们。在美国,根据2006年《卡特里娜飓风后紧急管理改革法案》,州或地方政府有责任为所有人群协调疏散计划。这些包括残疾人。但是,很少有计划为行动不便的人制定计划。在短暂的灾难中将行动不便的人从他们的个人位置撤离的问题是一个极具挑战性的组合优化问题。为了开发模型并选择解决方案,我们调查了相关文献。根据我们的评论,我们提出了问题,并开发了蚁群优化(ACO)算法来解决该问题。然后,我们在具有几个不同参数设置的五个风格化数据集上测试两种不同版本的ACO算法。

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