Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management
Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management
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DOI：
10.1016/j.ress.2018.09.011
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
S. Nozhati;Yugandhar Sarkale;B. Ellingwood;E. Chong;H. Mahmoud
中科院分区：
文献类型：
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作者：
S. Nozhati;Yugandhar Sarkale;B. Ellingwood;E. Chong;H. Mahmoud
社区一级缺乏全面决策办法是一个重要问题，需要立即予以注意。网络级决策算法需要解决大规模优化问题，这对计算提出了挑战。当考虑各种不确定性来源时，优化问题的复杂性增加。本研究介绍了一个连续的离散优化方法，作为一个决策框架，在社区层面的恢复管理。所提出的数学方法利用近似动态规划沿着与用于确定恢复动作的逻辑学。我们的方法克服了维度灾难，并在灾难发生后管理多状态，大规模的基础设施系统。我们还提供了计算结果表明，我们的方法不仅包括恢复政策的负责任的公共和私人实体在社区内，但也大大提高了他们的基本战略与有限的资源的性能。该方法可以有效地实施，以确定近最佳的恢复决策后，严重的地震的基础上，多个目标的电力网络的试验台社区后，美国加州吉尔罗伊粗略建模。建议的优化方法支持风险知情的社区决策者在混乱的灾后环境。
The lack of a comprehensive decision-making approach at the community level is an important problem that warrants immediate attention. Network-level decision-making algorithms need to solve large-scale optimization problems that pose computational challenges. The complexity of the optimization problems increases when various sources of uncertainty are considered. This research introduces a sequential discrete optimization approach, as a decision-making framework at the community level for recovery management. The proposed mathematical approach leverages approximate dynamic programming along with heuristics for the determination of recovery actions. Our methodology overcomes the curse of dimensionality and manages multi-state, large-scale infrastructure systems following disasters. We also provide computational results showing that our methodology not only incorporates recovery policies of responsible public and private entities within the community but also substantially enhances the performance of their underlying strategies with limited resources. The methodology can be implemented efficiently to identify near-optimal recovery decisions following a severe earthquake based on multiple objectives for an electrical power network of a testbed community coarsely modeled after Gilroy, California, United States. The proposed optimization method supports risk-informed community decision makers within chaotic post-hazard circumstances.