Stochastic optimal control methodologies in risk-informed community resilience planning: Fid-Bau Portal

Stochastic optimal control methodologies in risk-informed community resilience planning

Nozhati, Saeed / Ellingwood, Bruce R. / Chong, Edwin K.P.

Highlights • The concepts of dynamic optimization in community resilience and recovery are introduced. • The study formulates post-disaster community recovery within Markov Decision Process framework. • Different model-based and model-free approximate dynamic programming techniques are probed. • The study addresses the twin curses of dimensionality and modeling for large communities.

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.