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首页> 外文期刊>Journal of Management in Engineering >Prioritizing Postdisaster Recovery of Transportation Infrastructure Systems Using Multiagent Reinforcement Learning
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Prioritizing Postdisaster Recovery of Transportation Infrastructure Systems Using Multiagent Reinforcement Learning

机译：利用多轴加固学习优先考虑运输基础设施系统的邮政恢复

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摘要

Postdisaster reconstruction of transportation infrastructures generally entails complex and multiobjective planning and implementation options under uncertainty because of a large number of underlying subjective and objective factors, including social, economic, political, and technical aspects. With limited federal, state, and local resources, it is also challenging for decision-makers to establish a meticulous plan for postdisaster transportation recovery. However, previous studies mainly dealt with the specific planning or execution part of the postdisaster recovery process and rarely considered a comprehensive set of objectives in their investigations. This paper aims to develop a new prioritization approach for rapid and optimized postdisaster recovery that evaluates recovery priorities of damaged transportation infrastructure systems and affected regions through a multiagent system using a reinforcement learning technique. The proposed model contributes to the body of knowledge by providing a new optimization framework, considering transportation network recovery, and minimizing the social impact of the current prolonged recovery process on affected communities. This new methodology is expected to help public agencies make an informed decision for distributing given resources and structurally arranging disaster recovery processes of transportation systems by simulating real-world high-dimensional disaster scenarios and optimizing their recovery plans. In particular, the proposed approach pursues to assist disaster-relevant practitioners in considering a holistic perspective for comprehensive decision-making, incorporating diverse factors of planning transportation recovery and assigning their resources according to socioeconomic factors of affected communities.
机译：由于大量潜在的主观性和客观因素，包括社会，经济，政治和技术方面，运输基础设施的后期运输基础设施通常需要复杂和多目标规划和实施方案，包括社会，经济，政治和技术方面。凭借有限的联邦，州和当地资源，决策者也挑战了建立后期运输恢复的一丝不苟。然而，之前的研究主要处理了邮递员恢复过程的具体规划或执行部分，并且很少考虑在调查中综合综合目标。本文旨在开发新的优先考虑方法，用于快速和优化的岗位恢复，通过使用加强学习技术通过多算系统评估损坏的运输基础设施系统和受影响地区的恢复优先级。通过提供新的优化框架，考虑运输网络恢复，并最大限度地减少当前延长恢复过程的社会影响，拟议的模型贡献了知识体系。这一新方法有望帮助公共机构通过模拟现实世界的高维灾害场景并优化其恢复计划，通过模拟现实世界的高维灾害，并在结构上安排交通系统的灾难恢复过程的明智决定。特别是，拟议的方法追求有助于灾害 - 相关的从业人员考虑全面决策的整体角度，纳入各种各样的规划运输恢复因素，根据受影响社区的社会经济因素分配资源。

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  • 来源
    《Journal of Management in Engineering》 |2021年第1期|04020100.1-04020100.13|共13页
  • 作者

    Pedram Ghannad; Yong-Cheol Lee; Jin Ouk Choi;

  • 作者单位

    Bert S. Turner Dept. of Construction Management Louisiana State Univ. Baton Rouge LA 70803;

    Bert S. Turner Dept. of Construction Management Louisiana State Univ. Baton Rouge LA 70803;

    Dept. of Civil and Environmental Engineering and Construction Univ. of Nevada Las Vegas VA 89154;

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