Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints
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DOI: 10.1016/j.ress.2021.107551
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- B. Castanier & C. Bérenguer & A. Grall, 2003. "A sequential condition‐based repair/replacement policy with non‐periodic inspections for a system subject to continuous wear," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 19(4), pages 327-347, October.
- Robin P. Nicolai & Rommert Dekker, 2008.
"Optimal Maintenance of Multi-component Systems: A Review,"
Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 11, pages 263-286,
Springer.
- Nicolai, R.P. & Dekker, R., 2006. "Optimal maintenance of multi-component systems: a review," Econometric Institute Research Papers EI 2006-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Memarzadeh, Milad & Pozzi, Matteo & Kolter, J. Zico, 2016. "Hierarchical modeling of systems with similar components: A framework for adaptive monitoring and control," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 159-169.
- Papakonstantinou, K.G. & Shinozuka, M., 2014. "Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 214-224.
- Bocchini, Paolo & Frangopol, Dan M., 2011. "A probabilistic computational framework for bridge network optimal maintenance scheduling," Reliability Engineering and System Safety, Elsevier, vol. 96(2), pages 332-349.
- Yang, David Y. & Frangopol, Dan M., 2019. "Life-cycle management of deteriorating civil infrastructure considering resilience to lifetime hazards: A general approach based on renewal-reward processes," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 197-212.
- Liu, Yu & Chen, Yiming & Jiang, Tao, 2020. "Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach," European Journal of Operational Research, Elsevier, vol. 283(1), pages 166-181.
- 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).
- Rocchetta, R. & Bellani, L. & Compare, M. & Zio, E. & Patelli, E., 2019. "A reinforcement learning framework for optimal operation and maintenance of power grids," Applied Energy, Elsevier, vol. 241(C), pages 291-301.
- Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
- Papakonstantinou, K.G. & Shinozuka, M., 2014. "Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 202-213.
- Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
- Andriotis, C.P. & Papakonstantinou, K.G., 2019. "Managing engineering systems with large state and action spaces through deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
- Daniel S. Bernstein & Robert Givan & Neil Immerman & Shlomo Zilberstein, 2002. "The Complexity of Decentralized Control of Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 27(4), pages 819-840, November.
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- Cheng, Jianda & Cheng, Minghui & Liu, Yan & Wu, Jun & Li, Wei & Frangopol, Dan M., 2024. "Knowledge transfer for adaptive maintenance policy optimization in engineering fleets based on meta-reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
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- da Costa, Paulo & Verleijsdonk, Peter & Voorberg, Simon & Akcay, Alp & Kapodistria, Stella & van Jaarsveld, Willem & Zhang, Yingqian, 2023. "Policies for the dynamic traveling maintainer problem with alerts," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1141-1152.
- Kıvanç, İpek & Özgür-Ünlüakın, Demet & Bilgiç, Taner, 2022. "Maintenance policy analysis of the regenerative air heater system using factored POMDPs," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
- Azar, Kamyar & Hajiakhondi-Meybodi, Zohreh & Naderkhani, Farnoosh, 2022. "Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
- Tseremoglou, Iordanis & Santos, Bruno F., 2024. "Condition-Based Maintenance scheduling of an aircraft fleet under partial observability: A Deep Reinforcement Learning approach," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
- Lee, Juseong & Mitici, Mihaela, 2023. "Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
- Nguyen, Van-Thai & Do, Phuc & Vosin, Alexandre & Iung, Benoit, 2022. "Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
- Najafi, Seyedvahid & Lee, Chi-Guhn, 2023. "A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
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- Ye, Zhenggeng & Cai, Zhiqiang & Yang, Hui & Si, Shubin & Zhou, Fuli, 2023. "Joint optimization of maintenance and quality inspection for manufacturing networks based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
- Mikhail, Mina & Ouali, Mohamed-Salah & Yacout, Soumaya, 2024. "A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
- Zheng, Meimei & Su, Zhiyun & Wang, Dong & Pan, Ershun, 2024. "Joint maintenance and spare part ordering from multiple suppliers for multicomponent systems using a deep reinforcement learning algorithm," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
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- Kamariotis, Antonios & Tatsis, Konstantinos & Chatzi, Eleni & Goebel, Kai & Straub, Daniel, 2024. "A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
- Mohammadi, Reza & He, Qing, 2022. "A deep reinforcement learning approach for rail renewal and maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
- Arcieri, Giacomo & Hoelzl, Cyprien & Schwery, Oliver & Straub, Daniel & Papakonstantinou, Konstantinos G. & Chatzi, Eleni, 2023. "Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
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Keywords
Inspection and maintenance planning; System risk and reliability; Constrained stochastic optimization; Partially observable Markov decision processes; Deep reinforcement learning; Decentralized multi-agent control;All these keywords.
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