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Designing a risk-informed balanced system by genetic algorithms: Comparison of different balancing criteria

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  • Podofillini, Luca
  • Zio, Enrico

Abstract

This paper deals with the use of importance measures (IMs) for the risk-informed optimization of system design and operation. It builds on previous work by the authors in which IMs are incorporated in the formulation of a genetic algorithm (GA) multi-objective optimization problem to drive the design towards a solution which is ‘balanced’ in the importance values of the components. This allows designing systems that are optimal from the point of view of economics and safety, without excessively low- or unnecessarily high-performing components.

Suggested Citation

  • Podofillini, Luca & Zio, Enrico, 2008. "Designing a risk-informed balanced system by genetic algorithms: Comparison of different balancing criteria," Reliability Engineering and System Safety, Elsevier, vol. 93(12), pages 1842-1852.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:12:p:1842-1852
    DOI: 10.1016/j.ress.2008.03.015
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    References listed on IDEAS

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    1. Zio, Enrico & Podofillini, Luca, 2007. "Importance measures and genetic algorithms for designing a risk-informed optimally balanced system," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1435-1447.
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    Cited by:

    1. 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.
    2. 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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