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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.
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Optimal stochastic dynamic scheduling for managing community recovery from natural hazards
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.
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- Award ID(s):
- 1638284
- PAR ID:
- 10190170
- Date Published:
- Journal Name:
- Reliability engineering systems safety
- Volume:
- 193
- ISSN:
- 0951-8320
- Page Range / eLocation ID:
- 106627
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1016/j.ress.2019.106627
