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Genetic algorithms for condition-based maintenance optimization under uncertainty

Abstract : This paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GA), 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.
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https://hal-supelec.archives-ouvertes.fr/hal-01269867
Contributor : Yanfu Li <>
Submitted on : Friday, February 5, 2016 - 1:57:01 PM
Last modification on : Wednesday, July 15, 2020 - 10:00:02 AM
Long-term archiving on: : Saturday, November 12, 2016 - 11:03:18 AM

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M. Compare, F. Martini, Enrico Zio. Genetic algorithms for condition-based maintenance optimization under uncertainty. European Journal of Operational Research, Elsevier, 2015, 244 (2), pp.611-623. ⟨10.1016/j.ejor.2015.01.057⟩. ⟨hal-01269867⟩

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