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Importance measures and genetic algorithms for designing a risk-informed optimally balanced system

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  • Zio, Enrico
  • Podofillini, Luca

Abstract

This paper deals with the use of importance measures for the risk-informed optimization of system design and management. An optimization approach is presented in which the information provided by the importance measures is incorporated in the formulation of a multi-objective optimization problem to drive the design towards a solution which, besides being optimal from the points of view of economics and safety, is also ‘balanced’ in the sense that all components have similar importance values. The approach allows identifying design systems without bottlenecks or unnecessarily high-performing components and with test/maintenance activities calibrated according to the components’ importance ranking. The approach is tested at first against a multi-state system design optimization problem in which off-the-shelf components have to be properly allocated. Then, the more realistic problem of risk-informed optimization of the technical specifications of a safety system of a nuclear power plant is addressed.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:10:p:1435-1447
    DOI: 10.1016/j.ress.2006.09.011
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    Citations

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    Cited by:

    1. 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.
    2. Lin, Chaochao & Song, Junho & Pozzi, Matteo, 2022. "Optimal inspection of binary systems via Value of Information analysis," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Whitson, John C. & Ramirez-Marquez, Jose Emmanuel, 2009. "Resiliency as a component importance measure in network reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1685-1693.
    4. Cristina Johansson & Johan Ölvander & Micael Derelöv, 2018. "Multi-objective optimization for safety and reliability trade-off: Optimization and results processing," Journal of Risk and Reliability, , vol. 232(6), pages 661-676, December.
    5. Chenxi Liu & Nan Chen & Jianing Yang, 2015. "New method for multi-state system reliability analysis based on linear algebraic representation," Journal of Risk and Reliability, , vol. 229(5), pages 469-482, October.
    6. Zhang, Sai & Du, Mengyu & Tong, Jiejuan & Li, Yan-Fu, 2019. "Multi-objective optimization of maintenance program in multi-unit nuclear power plant sites," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 532-548.
    7. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2017. "Portfolio optimization of safety measures for reducing risks in nuclear systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 20-29.
    8. Compare, Michele & Bellani, Luca & Zio, Enrico, 2019. "Optimal allocation of prognostics and health management capabilities to improve the reliability of a power transmission network," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 164-180.

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