IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v91y2006i9p1027-1038.html
   My bibliography  Save this article

Use of multiple objective evolutionary algorithms in optimizing surveillance requirements

Author

Listed:
  • Martorell, S.
  • Carlos, S.
  • Villanueva, J.F.
  • Sanchez, A.I
  • Galvan, B.
  • Salazar, D.
  • Cepin, M.

Abstract

This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related equipment. TI and TP are part of the Surveillance Requirements within Technical Specifications at Nuclear Power Plants. It addresses the problem of multi-objective optimization in the space of dependable variables, i.e. TI and TP, using a novel flexible structure of the optimization algorithm. Lessons learnt from the cases of application of the methodology to optimize TI and TP for the High-Pressure Injection System are given. The results show that the double-loop Multiple Objective Evolutionary Algorithm is able to find the Pareto set of solutions that represents a surface of non-dominated solutions that satisfy all the constraints imposed on the objective functions and decision variables. Decision makers can adopt then the best solution found depending on their particular preference, e.g. minimum cost, minimum unavailability.

Suggested Citation

  • Martorell, S. & Carlos, S. & Villanueva, J.F. & Sanchez, A.I & Galvan, B. & Salazar, D. & Cepin, M., 2006. "Use of multiple objective evolutionary algorithms in optimizing surveillance requirements," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1027-1038.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:9:p:1027-1038
    DOI: 10.1016/j.ress.2005.11.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832005002036
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2005.11.038?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Borysiewicz, Mieczysław & Kowal, Karol & Potempski, Sławomir, 2015. "An application of the value tree analysis methodology within the integrated risk informed decision making for the nuclear facilities," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 113-119.
    2. 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.
    3. Martón, I. & Martorell, P. & Mullor, R. & Sánchez, A.I. & Martorell, S., 2016. "Optimization of test and maintenance of ageing components consisting of multiple items and addressing effectiveness," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 151-158.
    4. KanÄ ev, DuÅ¡ko & ÄŒepin, Marko & Gjorgiev, Blaže, 2014. "Development and application of a living probabilistic safety assessment tool: Multi-objective multi-dimensional optimization of surveillance requirements in NPPs considering their ageing," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 135-147.
    5. 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.
    6. Traore, M. & Chammas, A. & Duviella, E., 2015. "Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 120-131.
    7. 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.

    More about this item

    Statistics

    Access and download statistics

    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:eee:reensy:v:91:y:2006:i:9:p:1027-1038. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.