IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v222y2008i2p153-160.html
   My bibliography  Save this article

A multi-objective genetic algorithm for RAMS+C optimization with uncertain decision variables

Author

Listed:
  • S Martorell
  • A Sánchez
  • J F Villanueva
  • S Carlos
  • V Serradell

Abstract

Surveillance requirements applied to safety systems of a nuclear power plant are established by the technical specification (TS). These requirements impose the surveillance test intervals (STIs) performed to ensure that safety-related systems normally in standby are ready to operate on demand. There is a great interest in the optimization of STIs, as they have a great effect on plant risk and also on the resources necessary to implement a certain surveillance test strategy. Operational experience at nuclear power plants shows that tests are not performed at a constant interval. Thus, it is a more realistic choice to consider the STI as a random variable rather than a constant value, as it has usually been considered. This paper focuses on the STI optimization based on risk and cost criteria and considering that the test intervals can be performed within a tolerance range.

Suggested Citation

  • S Martorell & A Sánchez & J F Villanueva & S Carlos & V Serradell, 2008. "A multi-objective genetic algorithm for RAMS+C optimization with uncertain decision variables," Journal of Risk and Reliability, , vol. 222(2), pages 153-160, June.
    • Handle: RePEc:sae:risrel:v:222:y:2008:i:2:p:153-160
    DOI: 10.1243/1748006XJRR140
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1243/1748006XJRR140
    Download Restriction: no
    File URL: https://libkey.io/10.1243/1748006XJRR140?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Marseguerra, M. & Zio, E. & Martorell, S., 2006. "Basics of genetic algorithms optimization for RAMS applications," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 977-991.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Martorell, S. & Villamizar, M. & Martón, I. & Villanueva, J.F. & Carlos, S. & Sánchez, A.I., 2014. "Evaluation of risk impact of changes to surveillance requirements addressing model and parameter uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 153-165.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    2. Peiravi, Abdossaber & Nourelfath, Mustapha & Zanjani, Masoumeh Kazemi, 2022. "Universal redundancy strategy for system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. E Zio & F Di Maio & S Martorell, 2008. "Fusion of artificial neural networks and genetic algorithms for multi-objective system reliability design optimization," Journal of Risk and Reliability, , vol. 222(2), pages 115-126, June.
    4. Cook, Jason L. & Ramirez-Marquez, Jose Emmanuel, 2009. "Optimal design of cluster-based ad-hoc networks using probabilistic solution discovery," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 218-228.
    5. Zio, E. & Pedroni, N., 2010. "An optimized Line Sampling method for the estimation of the failure probability of nuclear passive systems," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1300-1313.
    6. L Podofillini & E Zio, 2008. "Events group risk importance by genetic algorithms," Journal of Risk and Reliability, , vol. 222(3), pages 337-346, September.
    7. Anil Kr. Aggarwal & Vikram Singh & Sanjeev Kumar, 2017. "Availability analysis and performance optimization of a butter oil production system: a case study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 538-554, January.
    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.
    9. Okafor, Ekene Gabriel & Sun, You-Chao, 2012. "Multi-objective optimization of a series–parallel system using GPSIA," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 61-71.
    10. Mellal, Mohamed Arezki & Al-Dahidi, Sameer & Williams, Edward J., 2020. "System reliability optimization with heterogeneous components using hosted cuckoo optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    11. Longhi, Antonio Eduardo Bier & Pessoa, Artur Alves & Garcia, Pauli Adriano de Almada, 2015. "Multiobjective optimization of strategies for operation and testing of low-demand safety instrumented systems using a genetic algorithm and fault trees," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 525-538.
    12. Edoardo Patelli & Gerhart Schuëller, 2012. "Computational optimization strategies for the simulation of random media and components," Computational Optimization and Applications, Springer, vol. 53(3), pages 903-931, December.
    13. Zio, E. & Baraldi, P. & Pedroni, N., 2009. "Optimal power system generation scheduling by multi-objective genetic algorithms with preferences," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 432-444.
    14. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2009. "Modelling and optimization of proof testing policies for safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 838-854.
    15. Di Maio, Francesco & Marchetti, Stefano & Zio, Enrico, 2023. "Robust multi-objective optimization of safety barriers performance parameters for NaTech scenarios risk assessment and management," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    16. Fiondella, Lance & Lin, Yi-Kuei & Pham, Hoang & Chang, Ping-Chen & Li, Chendong, 2017. "A confidence-based approach to reliability design considering correlated failures," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 102-114.
    17. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2009. "Design optimization of a safety-instrumented system based on RAMS+C addressing IEC 61508 requirements and diverse redundancy," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 162-179.
    18. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2012. "Multi-objective optimization of design and testing of safety instrumented systems with MooN voting architectures using a genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 45-60.
    19. Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.
    20. Peiravi, Abdossaber & Ardakan, Mostafa Abouei & Zio, Enrico, 2020. "A new Markov-based model for reliability optimization problems with mixed redundancy strategy," Reliability Engineering and System Safety, Elsevier, vol. 201(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:222:y:2008:i:2:p:153-160. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.