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Determination of Pareto frontier in multi-objective maintenance optimization

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  • Certa, Antonella
  • Galante, Giacomo
  • Lupo, Toni
  • Passannanti, Gianfranco

Abstract

The objective of a maintenance policy generally is the global maintenance cost minimization that involves not only the direct costs for both the maintenance actions and the spare parts, but also those ones due to the system stop for preventive maintenance and the downtime for failure. For some operating systems, the failure event can be dangerous so that they are asked to operate assuring a very high reliability level between two consecutive fixed stops. The present paper attempts to individuate the set of elements on which performing maintenance actions so that the system can assure the required reliability level until the next fixed stop for maintenance, minimizing both the global maintenance cost and the total maintenance time. In order to solve the previous constrained multi-objective optimization problem, an effective approach is proposed to obtain the best solutions (that is the Pareto optimal frontier) among which the decision maker will choose the more suitable one. As well known, describing the whole Pareto optimal frontier generally is a troublesome task. The paper proposes an algorithm able to rapidly overcome this problem and its effectiveness is shown by an application to a case study regarding a complex series–parallel system.

Suggested Citation

  • Certa, Antonella & Galante, Giacomo & Lupo, Toni & Passannanti, Gianfranco, 2011. "Determination of Pareto frontier in multi-objective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 861-867.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:7:p:861-867
    DOI: 10.1016/j.ress.2010.12.019
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    References listed on IDEAS

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    1. Lapa, Celso Marcelo F. & Pereira, Cláudio Márcio N.A. & de Barros, Márcio Paes, 2006. "A model for preventive maintenance planning by genetic algorithms based in cost and reliability," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 233-240.
    2. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    3. Galante, Giacomo & Passannanti, Gianfranco, 2009. "An exact algorithm for preventive maintenance planning of series–parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1517-1525.
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    Cited by:

    1. Fritzsche, R. & Gupta, J.N.D. & Lasch, R., 2014. "Optimal prognostic distance to minimize total maintenance cost: The case of the airline industry," International Journal of Production Economics, Elsevier, vol. 151(C), pages 76-88.
    2. W Zhu & M Fouladirad & C Bérenguer, 2015. "Bi-criteria maintenance policies for a system subject to competing wear and δ-shock failures," Journal of Risk and Reliability, , vol. 229(6), pages 485-500, December.
    3. Syan, Chanan S. & Ramsoobag, Geeta, 2019. "Maintenance applications of multi-criteria optimization: A review," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    4. Briš, Radim & Byczanski, Petr, 2013. "Effective computing algorithm for maintenance optimization of highly reliable systems," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 77-85.
    5. Maaroufi, Ghofrane & Chelbi, Anis & Rezg, Nidhal, 2013. "Optimal selective renewal policy for systems subject to propagated failures with global effect and failure isolation phenomena," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 61-70.
    6. Briš, Radim & Byczanski, Petr & Goňo, Radomír & Rusek, Stanislav, 2017. "Discrete maintenance optimization of complex multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 80-89.

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