  The functioning of interdependent civil infrastructure systems in the
aftermath of a disruptive event is critical to the performance and vitality of
any modern urban community. Post-event stressors and chaotic circumstances,
time limitations, and complexities in the community recovery process highlight
the necessity for a comprehensive decision-making framework at the
community-level for post-event recovery management. Such a framework must be
able to handle large-scale scheduling and decision processes, which involve
difficult control problems with large combinatorial decision spaces. This study
utilizes approximate dynamic programming algorithms along with heuristics for
the identification of optimal community recovery actions following the
occurrence of an extreme earthquake event. The proposed approach addresses the
curse of dimensionality in its analysis and management of multi-state,
large-scale infrastructure systems. Furthermore, the proposed approach can
consider the cur-rent recovery policies of responsible public and private
entities within the community and shows how their performance might be
improved. A testbed community coarsely modeled after Gilroy, California, is
utilized as an illustrative example. While the illustration provides optimal
policies for the Electrical Power Network serving Gilroy following a severe
earthquake, preliminary work shows that the methodology is computationally well
suited to other infrastructure systems and hazards.
