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Genetic algorithms for condition-based maintenance optimization under uncertainty

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  • Compare, M.
  • Martini, F.
  • Zio, E.

Abstract

This paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GAs), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept. The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant.

Suggested Citation

  • Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.
    • Handle: RePEc:eee:ejores:v:244:y:2015:i:2:p:611-623
    DOI: 10.1016/j.ejor.2015.01.057
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    9. Peng, Rui & Liu, Bin & Zhai, Qingqing & Wang, Wenbin, 2019. "Optimal maintenance strategy for systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 624-632.
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    11. Efraim Laksman & Ann-Brith Strömberg & Michael Patriksson, 2020. "The stochastic opportunistic replacement problem, part III: improved bounding procedures," Annals of Operations Research, Springer, vol. 292(2), pages 711-733, September.
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    14. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2024. "Optimal task aborting and sequencing in time constrained multi-task multi-attempt missions," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
    16. Martón, I. & Martorell, P. & Mullor, R. & Sánchez, A.I. & Martorell, S., 2016. "Optimization of test and maintenance of ageing components consisting of multiple items and addressing effectiveness," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 151-158.
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    18. Levitin, Gregory & Finkelstein, Maxim & Xiang, Yanping, 2020. "Optimal aborting rule in multi-attempt missions performed by multicomponent systems," European Journal of Operational Research, Elsevier, vol. 283(1), pages 244-252.
    19. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
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    21. Radim Briš & Nuong Thi Thuy Tran, 2023. "Discrete Model for a Multi-Objective Maintenance Optimization Problem of Safety Systems," Mathematics, MDPI, vol. 11(2), pages 1-18, January.

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