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Redundancy allocation problem of a system with increasing failure rates of components based on Weibull distribution: A simulation-based optimization approach

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  • Guilani, Pedram Pourkarim
  • Azimi, Parham
  • Niaki, S.T.A.
  • Niaki, Seyed Armin Akhavan

Abstract

The redundancy allocation problem (RAP) is a useful method to enhance system reliability. In most works involving RAP, failure rates of the system components are assumed to follow either exponential or k-Erlang distributions. In real world problems however, many systems have components with increasing failure rates. This indicates that as time passes by, the failure rates of the system components increase in comparison to their initial failure rates. In this paper, the redundancy allocation problem of a series–parallel system with components having an increasing failure rate based on Weibull distribution is investigated. An optimization method via simulation is proposed for modeling and a genetic algorithm is developed to solve the problem.

Suggested Citation

  • Guilani, Pedram Pourkarim & Azimi, Parham & Niaki, S.T.A. & Niaki, Seyed Armin Akhavan, 2016. "Redundancy allocation problem of a system with increasing failure rates of components based on Weibull distribution: A simulation-based optimization approach," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 187-196.
    • Handle: RePEc:eee:reensy:v:152:y:2016:i:c:p:187-196
    DOI: 10.1016/j.ress.2016.03.010
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    References listed on IDEAS

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    1. Chambari, Amirhossain & Najafi, Amir Abbas & Rahmati, Seyed Habib A. & Karimi, Aida, 2013. "An efficient simulated annealing algorithm for the redundancy allocation problem with a choice of redundancy strategies," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 158-164.
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    6. Dolatshahi-Zand, Ali & Khalili-Damghani, Kaveh, 2015. "Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 11-21.
    7. Zhang, Enze & Chen, Qingwei, 2016. "Multi-objective reliability redundancy allocation in an interval environment using particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 83-92.
    8. Kong, Xiangyong & Gao, Liqun & Ouyang, Haibin & Li, Steven, 2015. "Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 147-158.
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    Cited by:

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    10. Gholinezhad, Hadi & Zeinal Hamadani, Ali, 2017. "A new model for the redundancy allocation problem with component mixing and mixed redundancy strategy," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 66-73.
    11. Guilani, Pardis Pourkarim & Juybari, Mohammad N. & Ardakan, Mostafa Abouei & Kim, Heungseob, 2020. "Sequence optimization in reliability problems with a mixed strategy and heterogeneous backup scheme," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    12. Behzad Karimi & Seyed Taghi Akhavan Niaki & Amir Hossein Niknamfar & Mahsa Gareh Hassanlu, 2021. "Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm," Journal of Risk and Reliability, , vol. 235(2), pages 306-328, April.
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