An Approximate Dynamic Programming Approach to Community Recovery Management (Extended Abstract)

TL;DR

This paper presents an approximate dynamic programming framework for managing community recovery after disasters, effectively handling large decision spaces and integrating multiple infrastructure systems to optimize post-event recovery actions.

Contribution

It introduces a novel approximate dynamic programming approach that addresses the complexity of large-scale community recovery decision-making post-disaster.

Findings

Successfully models recovery policies for electrical power systems after earthquakes.

Demonstrates computational efficiency for large-scale infrastructure management.

Applicable to various infrastructure systems and hazard scenarios.

Abstract

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.