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.

中文翻译：

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地理风险因素对灾害大规模疏散策略的影响 – 一种智能混合优化


本文研究了不同地区地理风险不同的大规模城市紧急疏散网络设计 （EEND） 问题。要做出的决定是 （i） 确定活跃的避难所，（ii） 选择疏散路线，以及 （iii） 管理从配送中心到避难所的救援物资供应。易受洪水和飓风影响的区域被划分为多个区域，每个区域都有特定的脆弱性风险。对于每个区域，风险指标是通过结合风险因素来计算的 - 运送人员和救援物资以及临时避难所的放置。目标是最大限度地降低整个网络的最大风险，确保风险的均衡分配。开发了一种组合情景规划方法来管理灾害严重程度和疏散人员人数的不确定性。为了纳入不同的地理风险，开发、调整和验证了一种智能混合优化方法作为一种新的解决方案技术来解决 EEND 问题。所提出的方法使用专为 EEND 问题设计的有向本地搜索结构和基于 AI 的自参数调优模块，从而提高了性能。为了提取见解，法国雷恩被视为一个案例研究。结果表明，与传统的和风险目标相比，使用 min-max 公式可以减少伤亡。此外，增加城市区域数量的详细疏散计划可增强 EEND 性能。实际见解表明，将避难所的数量减少到容纳所有疏散人员所需的基本容量，因为额外的避难所可能会导致疏散和补给路线增加，可能在风险较高的地区。