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Cost Optimization and High Available Heterogeneous Series-Parallel Redundant System Design Using Genetic Algorithms

  • Walid ChaabanEmail author
  • Michael Schwarz
  • Josef Börcsök
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 343)

Abstract

Heterogeneous redundant series-parallel systems allow the mixing of components within the same subsystem. This diversity feature may improve the overall characteristics of the system compared with the homogeneous case in term of less susceptibility against so called common-cause failures and reduced cost. That means they guarantee longer availability and are quite suitable for systems that are designed to perform continuous processes. But the main challenging task is to determine the optimal design that corresponds to the minimal investment costs and satisfies the predefined constraints. This kind of combinatorial optimization tasks is perfectly solved using heuristic methods, since those approaches showed stability, powerfulness, and computing effectiveness in solving such matters. This task is more complex than the homogeneous case since the search space is getting larger due to the fact that every component available and that can be deployed in a subsystem has to be taken into account. This fact leads definitely to greater chromosome length and makes the search more time consuming. The algorithm has been implemented in Matlab and three different existing models (Levitin, Lisnianski, and Ouzineb) have been considered for a comparison with the homogeneous case and for validation purposes.

Keywords

Common cause failure (CCF) Genetic algorithms Heterogeneous series-parallel systems Redundancy allocation problem (RAP) Universal moment generating function (UMGF) 

References

  1. 1.
    Kuo, W., Rajendra Prasad, V., Tillman, F.A., Hwang, C.-L.: Optimal Reliability Design, Fundamentals and Applications. Cambridge University Press, Cambridge (2001)Google Scholar
  2. 2.
    Levitin, G., Lisnianski, A., Haim, H.B., Elmakis, D.: Genetic Algorithm and Universal Generating Function Technique for Solving Problems of Power System Reliability Optimization. The Israel Electric Corporation Ltd., Planning Development & Technology Division (2000)Google Scholar
  3. 3.
    Levitin, G., Lisnianski, A., Haim, H.B.: Redundancy optimization for series-parallel multi state systems. IEEE Trans. Reliab. 47(2) (1998)Google Scholar
  4. 4.
    Lisnianski, A., Livitin, G., Haim, H.B., Elmakis, D.: Power system optimization subject to reliability constraints. Electr. Power Syst. Res. 39, 145–152 (1996)CrossRefGoogle Scholar
  5. 5.
    Ouzineb, M.: Heuristiques éfficaces pour l’optimisation de la performance des systèmes séries-parallèles. Département d’informatique et de recherche opérationnelle Faculté des arts et des sciences, Université de Montréal, 2009Google Scholar
  6. 6.
    Ouzineb, M., Nourelfath, M., Gendreau, M.: Tabu search for the redundancy allocation problem of homogenous series-parallel multi-state systems. Reliab. Eng. Syst. Saf. 93, 1257–1272 (2008)CrossRefGoogle Scholar
  7. 7.
    Ouzineb, M., Nourelfath, M., Gendreau, M.: A heuristic method for non-homogeneous redundancy optimization of series-parallel multi-state systems. J. Heuristics 17(1), 1–22 (2009)CrossRefGoogle Scholar
  8. 8.
    Yalaoui, A., Chu, C., Châtelet, E.: Reliability allocation problem in a series–parallel system. Reliab. Eng. Syst. Saf. 90, 55–61 (2005)CrossRefGoogle Scholar
  9. 9.
    Li, C.-y., Chen, X., Yi, X.-s., Tao, J.-y.: Heterogeneous redundancy optimization for multi-state series–parallel systems subject to common cause failures. Reliab. Eng. Syst. Saf. 95, 202–207 (2010)CrossRefGoogle Scholar
  10. 10.
    Chaaban, W., Schwarz, M., Börcsök, J.: Budgetary and redundancy optimisation of homogeneous series-parallel systems subject to availability constraints using Matlab implemented genetic computing. In: 24th IET Irish, Signals and Systems Conference (ISSC 2013)Google Scholar
  11. 11.
    Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  12. 12.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)zbMATHGoogle Scholar
  13. 13.
    Tian, Z., Zuo, M.J., Huang, H.: Reliability-redundancy allocation for multi-state series-parallel systems. IEEE Trans. Reliab. 57(2), 303–310 (2008)CrossRefGoogle Scholar
  14. 14.
    Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming, Modern Concepts and Applications. CRC Press, Boca Raton (2009)zbMATHCrossRefGoogle Scholar
  15. 15.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd revised and extended edition. Springer, Berlin (2011)Google Scholar
  16. 16.
    Tillman, F.A., Hwang, C.-L., Kuo, W.: Optimization techniques for system reliability with redundancy—a review. IEEE Trans. Reliab. R-26(3), 148–155 (1977)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Levitin, G.: The Universal Generating Function in Reliability Analysis and Optimization. Springer, London (2005)Google Scholar
  18. 18.
    Chaaban, W., Schwarz, M., Börcsök, J.: Cost and redundancy optimization of homogeneous series-parallel multi-state systems subject to availability constraints using a Matlab implemented genetic algorithm. In: Recent Advances in Circuits, Systems and Automatic Control, WSEAS 2013, Budapest, Hungary, 2013Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Walid Chaaban
    • 1
    Email author
  • Michael Schwarz
    • 1
  • Josef Börcsök
    • 1
  1. 1.Department of Computer Architecture and System ProgrammingUniversity of KasselKasselGermany

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