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An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems

Author

Listed:
  • Seyed Mohsen Mousavi

    (Islamic Azad University)

  • Najmeh Alikar

    (University of Malaya)

  • Madjid Tavana

    (La Salle University
    University of Paderborn)

  • Debora Di Caprio

    (York University
    Polo Tecnologico IISS G. Galilei)

Abstract

In this study, we propose an improved particle swarm optimization algorithm (IPSOA) to solve discounted redundancy allocation problems (DRAPs) in series-parallel systems. The system consists of subsystems in series with parallel components in each subsystem. Homogeneous redundant components are used to achieve a desirable system reliability. The components of each subsystem are characterized by their cost, weight, and reliability where the cost is calculated under an all unit discount policy. The goal is to find the optimum combination of the components for each subsystem so that the system reliability is maximized. After formulating the mathematical model, the proposed IPOSA is implemented to achieve the solution. Moreover, an experimental design approach is used to calibrate the algorithm’s parameters. Three numerical problems, each of which considered under several configurations, are discussed to demonstrate the applicability of the proposed procedures. In order to evaluate the accuracy and performance of our IPSOA, all the problems are also solved using two other meta-heuristics, namely, the homogeneous particle swarm optimization algorithm and tabu search. The numerical results show that, when solving DRAPs, IPSOA behaves better than the other two algorithms considered from both a solution quality and a computational viewpoint.

Suggested Citation

  • Seyed Mohsen Mousavi & Najmeh Alikar & Madjid Tavana & Debora Di Caprio, 2019. "An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1175-1194, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1311-9
    DOI: 10.1007/s10845-017-1311-9
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    References listed on IDEAS

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    1. Ouzineb, Mohamed & Nourelfath, Mustapha & Gendreau, Michel, 2008. "Tabu search for the redundancy allocation problem of homogenous series–parallel multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1257-1272.
    2. Nahas, Nabil & Nourelfath, Mustapha & Ait-Kadi, Daoud, 2007. "Coupling ant colony and the degraded ceiling algorithm for the redundancy allocation problem of series–parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 92(2), pages 211-222.
    3. Tavakkoli-Moghaddam, R. & Safari, J. & Sassani, F., 2008. "Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 550-556.
    4. Ha, Chunghun & Kuo, Way, 2006. "Reliability redundancy allocation: An improved realization for nonconvex nonlinear programming problems," European Journal of Operational Research, Elsevier, vol. 171(1), pages 24-38, May.
    5. Liang, Yun-Chia & Chen, Yi-Ching, 2007. "Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 323-331.
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    Cited by:

    1. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    2. Cosmena Mahapatra & Ashish Payal & Meenu Chopra, 2020. "Swarm intelligence based centralized clustering: a novel solution," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1877-1888, December.
    3. Zhang, Zixuan & Yang, Lin & Xu, Youwei & Zhu, Ran & Cao, Yining, 2023. "A novel reliability redundancy allocation problem formulation for complex systems," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Ling, Chunyan & Yang, Lechang & Feng, Kaixuan & Kuo, Way, 2023. "Survival signature based robust redundancy allocation under imprecise probability," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    5. Wenjie Wang & Guangdong Tian & Gang Yuan & Duc Truong Pham, 2023. "Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1065-1083, March.
    6. Yishuang Hu & Yi Ding & Zhiguo Zeng, 2022. "Redundancy optimization for multi-state series-parallel systems using ordinal optimization-based-genetic algorithm," Journal of Risk and Reliability, , vol. 236(1), pages 66-78, February.
    7. Chunyan Ling & Jingzhe Lei & Way Kuo, 2024. "A sequential two-stage approach based on variational Bayesian inference for reliability-redundancy allocation," Journal of Risk and Reliability, , vol. 238(1), pages 136-157, February.

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