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Optimal stochastic dynamic scheduling for managing community recovery from natural hazards

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  • Nozhati, Saeed
  • Sarkale, Yugandhar
  • Chong, Edwin K.P.
  • Ellingwood, Bruce R.

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 policies 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 presented herein employs the rollout algorithm, which provides an effective computational tool for optimization problems dealing with real-world large-scale networks and communities. The proposed methodology is applied to a realistic community recovery problem, where different decision-making objectives are considered. The approach accommodates current restoration strategies employed in recovery management; computational results indicate that the restoration policies identified herein significantly outperform the current recovery strategies. Finally, the applicability of the method to address different risk attitudes of policymakers, which include risk-neutral and risk-averse attitudes in the community recovery management, is examined.

Suggested Citation

  • Nozhati, Saeed & Sarkale, Yugandhar & Chong, Edwin K.P. & Ellingwood, Bruce R., 2020. "Optimal stochastic dynamic scheduling for managing community recovery from natural hazards," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
  • Handle: RePEc:eee:reensy:v:193:y:2020:i:c:s0951832018315588
    DOI: 10.1016/j.ress.2019.106627
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    References listed on IDEAS

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    1. Shapiro, Alexander & Tekaya, Wajdi & da Costa, Joari Paulo & Soares, Murilo Pereira, 2013. "Risk neutral and risk averse Stochastic Dual Dynamic Programming method," European Journal of Operational Research, Elsevier, vol. 224(2), pages 375-391.
    2. Saeed Nozhati & Bruce R. Ellingwood & Hussam Mahmoud, 2019. "Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2127-2142, October.
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    7. Nozhati, Saeed & Sarkale, Yugandhar & Ellingwood, Bruce & K.P. Chong, Edwin & Mahmoud, Hussam, 2019. "Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 116-126.
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    Cited by:

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    4. Yang, Sen & Zhang, Yi & Lu, Xinzheng & Guo, Wei & Miao, Huiquan, 2024. "Multi-agent deep reinforcement learning based decision support model for resilient community post-hazard recovery," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Liu, Huan & Tatano, Hirokazu & Pflug, Georg & Hochrainer-Stigler, Stefan, 2021. "Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    6. Ferrario, E. & Poulos, A. & Castro, S. & de la Llera, J.C. & Lorca, A., 2022. "Predictive capacity of topological measures in evaluating seismic risk and resilience of electric power networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Fu, Yaping & Wu, Di & Wang, Yan & Wang, Hongfeng, 2020. "Facility location and capacity planning considering policy preference and uncertain demand under the One Belt One Road initiative," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 172-186.
    8. Alisjahbana, Irene & Graur, Andrei & Lo, Irene & Kiremidjian, Anne, 2022. "Optimizing strategies for post-disaster reconstruction of school systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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