Abstract The absorptive and restorative abilities of a community are two key elements of community resilience following disasters. The recovery of communities relies on an efficient restoration planning of damaged critical infrastructure systems, household units, and impaired supporting social and economic functions. These interdependent systems form a dynamic system of systems that changes continuously during restoration. Therefore, an effective and practical recovery planning process for a community can be modeled as a sequential dynamic optimization problem under uncertainty. This paper seeks to enhance our understanding of dynamic optimization concepts and their role in formulating post-disaster, community-level recovery strategies. Various methods of classic dynamic programming and reinforcement learning are examined and applied. Simulation-based approximate dynamic programming techniques are introduced to overcome the curse of dimensionality, which is characteristic of large-scale and multi-state systems of systems. The paper aims not only to study the unexplored topic of dynamic optimization in community resilience, but also to be a practical reference for policymakers, practitioners, engineers, and operations analysts to harness the power of dynamic optimization toward assessing and achieving community resilience.

中文翻译：

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风险知情社区复原力规划中的随机最优控制方法

摘要 社区的吸收能力和恢复能力是社区灾后恢复能力的两个关键要素。社区的恢复依赖于对受损的关键基础设施系统、家庭单元以及受损的支持性社会和经济功能进行有效的恢复规划。这些相互依赖的系统形成了一个动态系统，在恢复过程中不断变化。因此，社区的有效且实用的恢复规划过程可以建模为不确定性下的顺序动态优化问题。本文旨在增强我们对动态优化概念及其在制定灾后社区级恢复策略中的作用的理解。研究和应用了经典动态规划和强化学习的各种方法。引入基于仿真的近似动态规划技术来克服维数灾难，这是系统的大规模和多状态系统的特征。本文不仅旨在研究社区弹性中动态优化的未探索主题，而且还为决策者、从业者、工程师和运营分析师利用动态优化的力量评估和实现社区弹性提供实用参考。