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Solving advanced multi-objective robust designs by means of multiple objective evolutionary algorithms (MOEA): A reliability application

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  • Salazar A., Daniel E.
  • Rocco S., Claudio M.

Abstract

This paper extends the approach proposed by the second author in [Rocco et al. Robust design using a hybrid-cellular-evolutionary and interval-arithmetic approach: a reliability application. In: Tarantola S, Saltelli A, editors. SAMO 2001: Methodological advances and useful applications of sensitivity analysis. Reliab Eng Syst Saf 2003;79(2):149-59 [special issue]] to obtain a robust system design. The approach based on the use of evolutionary algorithms and interval arithmetic finds the maximum-volume inner box (MIB) or the maximal ranges of variation for each variable that preserve pre-specified design/performance requirements. The original single-objective formulation considers the definition of a MIB around a specified centroid (case 1), or around an unspecified centroid (case 2). In this paper, both cases were successfully modified and solved as multiple-objective (MO) problems, showing the advantages of MO formulations in a design-selection decision framework. Special attention is devoted to the unspecified centre MO problem where the computational efficiency could be a critical issue. In that sense, a new procedure based on the “percentage representation†is proposed. This approach reduces drastically the computational burden, extending the possibilities of use of robust design.

Suggested Citation

  • Salazar A., Daniel E. & Rocco S., Claudio M., 2007. "Solving advanced multi-objective robust designs by means of multiple objective evolutionary algorithms (MOEA): A reliability application," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 697-706.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:6:p:697-706
    DOI: 10.1016/j.ress.2006.03.003
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    References listed on IDEAS

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    1. Hendrix, Eligius M. T. & Mecking, Carmen J. & Hendriks, Theo H. B., 1996. "Finding robust solutions for product design problems," European Journal of Operational Research, Elsevier, vol. 92(1), pages 28-36, July.
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    Cited by:

    1. Stuart M. Harwood & Paul I. Barton, 2017. "How to solve a design centering problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 86(1), pages 215-254, August.
    2. Coelho, Leandro dos Santos, 2009. "An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 830-837.
    3. Roy, Bernard, 2010. "Robustness in operational research and decision aiding: A multi-faceted issue," European Journal of Operational Research, Elsevier, vol. 200(3), pages 629-638, February.

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