The effect of geographic risk factors on disaster mass evacuation strategies: A smart hybrid optimization

IF 8.3 1区 工程技术 Q1 ECONOMICS
Ahmad Jafarian , Tobias Andersson Granberg , Reza Zanjirani Farahani
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Abstract

This paper investigates an urban Emergency Evacuation Network Design (EEND) problem on a large scale when geographical risk in different areas varies. The decisions to make are (i) determining active shelters, (ii) selecting evacuation routes, and (iii) managing the supply of relief commodities from distribution centers to shelters. A region prone to floods and hurricanes is divided into zones, each with a specific vulnerability risk. For each zone, a risk measure is calculated by combining the risk factors –transporting people and relief commodities and the placement of temporary shelters. The objective is to minimize the maximum risk across the network, ensuring a balanced distribution of risk. A combinatorial scenario planning approach is developed to manage the uncertainty in disaster severity and the evacuee numbers. To incorporate varied geographical risks, a smart hybrid optimization approach as a new solution technique is developed, tuned, and validated to solve the EEND problem. The proposed approach uses directed local search structures designed for the EEND problem and an AI-based self-parameter tuning module, enhancing performance. To extract insights, Rennes, France, is considered a case study. The results indicate a reduction in casualties using a min–max formulation compared to traditional sum-risk objectives. Further, a detailed evacuation plan that increases the number of city regions enhances EEND performance. Practical insights suggest minimizing the number of shelters to the essential capacity needed to host all evacuees, as additional shelters may lead to increased evacuation and supply routes, potentially in areas with higher risk.
地理风险因素对灾害大规模疏散策略的影响:智能混合优化
本文研究了当不同地区的地理风险不同时的大规模城市紧急疏散网络设计(EEND)问题。需要做出的决策包括:(i) 确定活动避难所;(ii) 选择疏散路线;(iii) 管理从配送中心到避难所的救灾物资供应。易受洪水和飓风侵袭的地区被划分为若干区域,每个区域都有特定的脆弱性风险。对于每个区域,通过综合风险因素--人员和救灾物资的运输以及临时避难所的安置--计算出风险度量。目标是最大限度地降低整个网络的最大风险,确保风险的均衡分布。为管理灾害严重程度和疏散人数的不确定性,开发了一种组合情景规划方法。为了纳入不同的地理风险,开发了一种智能混合优化方法作为新的解决技术,并对其进行了调整和验证,以解决 EEND 问题。所提出的方法使用了为 EEND 问题设计的定向局部搜索结构和基于人工智能的自参数调整模块,从而提高了性能。为深入了解该方法,我们以法国雷恩市为案例进行了研究。结果表明,与传统的总和风险目标相比,使用最小-最大公式可减少伤亡人数。此外,增加城市区域数量的详细疏散计划也提高了 EEND 的性能。实际经验表明,避难所的数量应尽量减少到容纳所有疏散人员所需的基本能力,因为增加避难所可能会导致疏散和补给路线的增加,而且有可能是在风险较高的地区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
16.20
自引率
16.00%
发文量
285
审稿时长
62 days
期刊介绍: Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management. Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.
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