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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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