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A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem

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Abstract

This paper presents a novel hybrid genetic algorithm (GA)-particle swarm optimization (PSO) approach for reliability redundancy allocation problem (RRAP) in series, series–parallel, and complex (bridge) systems. The proposed approach maximizes overall system reliability while minimizing system cost, system weight and volume, simultaneously, under nonlinear constraints. To meet these objectives, an adaptive hybrid GA–PSO approach is developed to identify the optimal solutions and improve computation efficiency for these NP-hard problems. An illustrative example is applied to show the capability and effectiveness of the proposed approach. According to the results, in all three cases, reliability values are improved. Moreover, computational time and variance are decreased compared to the similar studies. The proposed approach could be helpful for engineers and managers to better understand their system reliability and performance, and also to reach a better configuration.

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References

  1. Kulturel-Konak S, Smith AE, Coit DW (2003) Efficiently solving the redundancy allocation problem using tabu search. IIE Trans 35(6):515–526

    Article  Google Scholar 

  2. Kuo W, Wan R (2007) Recent advances in optimal reliability allocation. Comput Intell Reliab Eng Stud Comput Intell 39:1–36

    Article  Google Scholar 

  3. Chern MS (1992) On the computational complexity of reliability redundancy allocation in a series system. Oper Res Lett 11(5):309–315

    Article  MathSciNet  MATH  Google Scholar 

  4. Hikita M, Nakagawa Y, Narihisa H (1992) Reliability optimization of systems by a surrogate constraints algorithm. IEEE Trans Reliab 41(3):473–480

    Article  MATH  Google Scholar 

  5. Levitin G (2007) Computational intelligence in reliability engineering: evolutionary techniques in reliability analysis and optimization. Springer, New York

    Google Scholar 

  6. Nahas N, Nourelfath M (2005) Ant system for reliability optimization of a series system with multiple-choice and budget constraints. Reliab Eng Syst Saf 87:1–12

    Article  Google Scholar 

  7. Shelokar PS, Jayaraman VK, Kulkarni BD (2002) Ant algorithm for single and multi objective reliability optimization problems. Qual Reliab Eng Int 18(6):497–514

    Article  Google Scholar 

  8. Suman B (2003) Simulated annealing-based multi-objective algorithm and their application for system reliability. Eng Optim 35(4):391–416

    Article  Google Scholar 

  9. Sung CS, Cho YK (2000) Reliability optimization of a series system with multiple-choice and budget constraints. Eur J Oper Res 48:158–171

    MathSciNet  Google Scholar 

  10. Yun WY, Kim JW (2004) Multi-level redundancy optimization in series systems. Comput Ind Eng 46(2):337–346

    Article  MathSciNet  Google Scholar 

  11. Zafiropoulos EP, Dialynas EN (2004) Reliability and cost optimization of electronic devices considering the component failure rate uncertainty. Reliab Eng Syst Saf 84:271–284

    Article  Google Scholar 

  12. Zhao R, Liu B (2004) Redundancy optimization problems with uncertainty of combining randomness and fuzziness. Eur J Oper Res 157:716–735

    Article  MATH  Google Scholar 

  13. Altumi AA, Philipose AM, Taboun SM (2000) Reliability optimisation of FMS with spare tooling. Int J Adv Manuf Technol 16:551–558

    Article  Google Scholar 

  14. Lee H, Kuo W, Ha C (2003) Comparison of max–min approach and NN method for reliability optimization of series–parallel system. J Syst Sci Syst Eng J Sci Syst Eng 12(1):39–48

    Article  Google Scholar 

  15. Lillo Rosa E, Neuts Marcel F (2000) Empirical optimization in a reliability problem. Methodol Comput Appl Probab 2(4):413–424

    Article  MathSciNet  MATH  Google Scholar 

  16. Pan Z, Chen L, Zhang G (2007) A neural network method for reliability optimizations of complex systems. Wuhan Univ J Natl Sci 12(1):139–142

    Article  MathSciNet  MATH  Google Scholar 

  17. Li D, Sun X, Mckinnon K (2005) An exact solution method for reliability optimization in complex systems. Ann Oper Res 133:129–148

    Article  MathSciNet  MATH  Google Scholar 

  18. Norkina VI, Onishchenko BO (2008) Reliability optimization of a complex system by the stochastic branch and bound method. Cybern Syst Anal 44(3):418–428

    Article  Google Scholar 

  19. Valdebenito MA, Schuëller GI (2010) A survey on approaches for reliability-based optimization. Struct Multidiscip Optim 42:645–663

    Article  MathSciNet  Google Scholar 

  20. Dengiz B, Altiparmak F, Smith AE (1997) Local search genetic algorithm for optimal design of reliable networks. IEEE Trans Evol Comput 1(3):179–188

    Article  Google Scholar 

  21. Tavakkoli-Moghaddama R, Safari J, Sassani F (2008) Reliability optimization of series–parallel systems with a choice of redundancy strategies using a genetic algorithm. Reliab Eng Syst Saf 93:550–556

    Article  Google Scholar 

  22. Ye Z, Li Z, Min X (2010) Some improvements on adaptive genetic algorithms for reliability-related applications. Reliab Eng Syst Saf 95:120–126

    Article  Google Scholar 

  23. Hsieh YC, Chen TC, Bricker DL (1998) Genetic algorithms for reliability design problems. Microelectron Reliab 38:1599–1605

    Article  Google Scholar 

  24. Kuo W, Prasad VR, Tillman FA, Hwang CL (2001) Optimal reliability design: fundamentals and applications. Cambridge University Press, Cambridge

    Google Scholar 

  25. Kim HG, Bae C (2006) Reliability-redundancy optimization using simulated annealing algorithms. J Qual Maint Eng 12(4):354–363

    Article  Google Scholar 

  26. Yokota T, Gen M, Li YX (1996) Genetic algorithm for non-linear mixed-integer programming and its applications. Comput Ind Eng 30(4):905–917

    Article  Google Scholar 

  27. Coelho LDS (2009) An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications. Reliab Eng Syst Saf 94:830–837

    Article  Google Scholar 

  28. Angeline PJ (1998) Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. In V. William Porto and et al., editors, Evolutionary Programming, vol. 1447 of Lecture Notes in Computer Science, pp. 601–610. Springer, 1998

  29. Eberhart RC, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization. In et. al. V. William Porto, editor, Evolutionary Programming, vol. 1447 of Lecture Notes in Computer Science, pp. 611–616. Springer, 1998

  30. Settles M, Soule T (2005) Breeding swarms: a GA/PSO hybrid. Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 161–168

  31. Xu Z, Kuo W, Lin HH (1990) Optimization limits in improving system reliability. IEEE Trans Reliab 39(1):51–60

    Article  MATH  Google Scholar 

  32. Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press

  33. Levitin G (2005) The universal generating function in reliability analysis and optimization. Springer, London

    Google Scholar 

  34. Eberhart RC, Kennedy JF (1995) A new optimizer using particle swarm theory. Proceeding of the 6th international symposium on micro machine and human science, pp. 39–43

  35. Kennedy JF, Eberhart RC (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948

    Article  Google Scholar 

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Authors and Affiliations

  1. Mathematics and Industrial Engineering Department, Ecole Polytechnique de Montreal, Québec, Canada

    V. Ebrahimipour

  2. Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    M. Sheikhalishahi, V. Ebrahimipour, H. Shiri, H. Zaman & M. Jeihoonian

Authors
  1. M. Sheikhalishahi
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  2. V. Ebrahimipour
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  3. H. Shiri
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  4. H. Zaman
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  5. M. Jeihoonian
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Corresponding author

Correspondence to V. Ebrahimipour.

Appendices

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Appendix III

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Sheikhalishahi, M., Ebrahimipour, V., Shiri, H. et al. A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem. Int J Adv Manuf Technol 68, 317–338 (2013). https://doi.org/10.1007/s00170-013-4730-6

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  • DOI: https://doi.org/10.1007/s00170-013-4730-6

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