Optimal Stochastic Dynamic Scheduling for Managing Community Recovery from Natural Hazards

TL;DR

This paper presents a stochastic optimization framework using Markov decision processes and rollout algorithms to compute optimal community recovery policies after natural hazards, outperforming current strategies and accommodating different risk attitudes.

Contribution

It introduces a novel MDP-based stochastic scheduling method with rollout algorithms for large-scale community recovery, considering multiple uncertainties and risk preferences.

Findings

Optimal policies outperform existing recovery strategies.

Method effectively handles large-scale, real-world community networks.

Applicable to risk-neutral and risk-averse decision-making.

Abstract

Following the occurrence of an extreme natural or man-made event, community recovery management should aim at providing optimal restoration policies for a community over a planning horizon. Calculating such optimal restoration polices in the presence of uncertainty poses significant challenges for community leaders. Stochastic scheduling for several interdependent infrastructure systems is a difficult control problem with huge decision spaces. The Markov decision process (MDP)-based optimization approach proposed in this study incorporates different sources of uncertainties to compute the restoration policies. The computation of optimal scheduling schemes using our method employs the rollout algorithm, which provides an effective computational tool for optimization problems dealing with real-world large-scale networks and communities. We apply the proposed methodology to a realistic community recovery problem, where different decision-making objectives are considered. Our approach accommodates current restoration strategies employed in recovery management. Our computational results indicate that the restoration policies calculated using our techniques significantly outperform the current recovery strategies. Finally, we study the applicability of our method to address different risk attitudes of policymakers, which include risk-neutral and risk-averse attitudes in the community recovery management.