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Genetic algorithms for condition-based maintenance optimization under uncertainty

M. Compare, F. Martini and E. Zio

European Journal of Operational Research, 2015, vol. 244, issue 2, 611-623

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.

Keywords: Maintenance optimization; Genetic algorithms; Uncertain fitness; Ranking; Pareto dominance (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:244:y:2015:i:2:p:611-623

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European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

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