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Genetic algorithm-based optimization of testing and maintenance under uncertain unavailability and cost estimation: A survey of strategies for harmonizing evolution and accuracy

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  • Villanueva, J.F.
  • Sanchez, A.I.
  • Carlos, S.
  • Martorell, S.

Abstract

This paper presents the results of a survey to show the applicability of an approach based on a combination of distribution-free tolerance interval and genetic algorithms for testing and maintenance optimization of safety-related systems based on unavailability and cost estimation acting as uncertain decision criteria. Several strategies have been checked using a combination of Monte Carlo (simulation)––genetic algorithm (search-evolution). Tolerance intervals for the unavailability and cost estimation are obtained to be used by the genetic algorithms. Both single- and multiple-objective genetic algorithms are used. In general, it is shown that the approach is a robust, fast and powerful tool that performs very favorably in the face of noise in the output (i.e. uncertainty) and it is able to find the optimum over a complicated, high-dimensional nonlinear space in a tiny fraction of the time required for enumeration of the decision space. This approach reduces the computational effort by means of providing appropriate balance between accuracy of simulation and evolution; however, negative effects are also shown when a not well-balanced accuracy–evolution couple is used, which can be avoided or mitigated with the use of a single-objective genetic algorithm or the use of a multiple-objective genetic algorithm with additional statistical information.

Suggested Citation

  • Villanueva, J.F. & Sanchez, A.I. & Carlos, S. & Martorell, S., 2008. "Genetic algorithm-based optimization of testing and maintenance under uncertain unavailability and cost estimation: A survey of strategies for harmonizing evolution and accuracy," Reliability Engineering and System Safety, Elsevier, vol. 93(12), pages 1830-1841.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:12:p:1830-1841
    DOI: 10.1016/j.ress.2008.03.014
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    Cited by:

    1. 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.
    2. Ye, Zhisheng & Li, Zhizhong & Xie, Min, 2010. "Some improvements on adaptive genetic algorithms for reliability-related applications," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 120-126.
    3. 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.

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