  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.
