Optimal stochastic dynamic scheduling for managing community recovery from natural hazards
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DOI: 10.1016/j.ress.2019.106627
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Cited by:
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- Andriotis, C.P. & Papakonstantinou, K.G., 2021. "Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
- Ulusan, Aybike & Ergun, Özlem, 2021. "Approximate dynamic programming for network recovery problems with stochastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
- 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).
- 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).
- 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).
- 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.
- 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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Keywords
Approximate dynamic programming; Community-level decision making; Community recovery management; Markov decision process; Optimization; Rollout;All these keywords.
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