Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management

TL;DR

This paper presents a novel approximate dynamic programming framework for community-level recovery management after disasters, effectively handling large-scale, uncertain infrastructure systems to improve decision-making.

Contribution

It introduces a sequential discrete optimization approach that overcomes computational challenges and incorporates recovery policies for large infrastructure networks post-disaster.

Findings

Method efficiently identifies near-optimal recovery actions.

Enhances performance of recovery strategies with limited resources.

Supports risk-informed decision-making in chaotic post-hazard scenarios.

Abstract

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.