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Hierarchical differential evolution for minimal cut sets identification: Application to nuclear safety systems

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  • Di Maio, Francesco
  • Baronchelli, Samuele
  • Zio, Enrico

Abstract

In this paper, we present a Hierarchical Differential Evolution (HDE) algorithm for minimal cut set (mcs) identification of coherent and non-coherent Fault Trees (FTs). In realistic application of large-size systems, problems may be encountered in handling a large number of gates and events. In this work, to avoid any approximation, mcs identification is originally transformed into a hierarchical optimization problem, stated as the search for the minimum combination of cut sets that can guarantee the best coverage of all the minterms that make the system fail: during the first step of the iterative search, a multiple-population, parallel search policy is used to expedite the convergence of the second step of the exploration algorithm. The proposed hierarchical method is applied to the Reactor Protection System (RPS) of a Pressurized Water Reactor (PWR) and to the the Airlock System (AS) of a CANadian Deuterium Uranium (CANDU) reactor. Results are evaluated with respect to the accuracy and computational demand of the solution found.

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  • Di Maio, Francesco & Baronchelli, Samuele & Zio, Enrico, 2014. "Hierarchical differential evolution for minimal cut sets identification: Application to nuclear safety systems," European Journal of Operational Research, Elsevier, vol. 238(2), pages 645-652.
    • Handle: RePEc:eee:ejores:v:238:y:2014:i:2:p:645-652
    DOI: 10.1016/j.ejor.2014.04.021
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    References listed on IDEAS

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    1. Bjorkman, Kim, 2013. "Solving dynamic flowgraph methodology models using binary decision diagrams," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 206-216.
    2. Borgonovo, E., 2010. "The reliability importance of components and prime implicants in coherent and non-coherent systems including total-order interactions," European Journal of Operational Research, Elsevier, vol. 204(3), pages 485-495, August.
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    4. Zio, Enrico & Di Maio, Francesco & Tong, Jiejuan, 2010. "Safety margins confidence estimation for a passive residual heat removal system," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 828-836.
    5. Gao, Xueli & Cui, Lirong & Li, Jinlin, 2007. "Analysis for joint importance of components in a coherent system," European Journal of Operational Research, Elsevier, vol. 182(1), pages 282-299, October.
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    Cited by:

    1. Francesco Di Maio & Samuele Baronchelli & Enrico Zio, 2015. "A Computational Framework for Prime Implicants Identification in Noncoherent Dynamic Systems," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 142-156, January.
    2. Zhao, Zhiwei & Yang, Jingming & Hu, Ziyu & Che, Haijun, 2016. "A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems," European Journal of Operational Research, Elsevier, vol. 250(1), pages 30-45.
    3. Hao, Yucheng & Jia, Limin & Zio, Enrico & Wang, Yanhui & He, Zhichao, 2023. "A multi-objective optimization model for identifying groups of critical elements in a high-speed train," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Borgonovo, E. & Cappelli, V. & Maccheroni, F. & Marinacci, M., 2018. "Risk analysis and decision theory: A bridge," European Journal of Operational Research, Elsevier, vol. 264(1), pages 280-293.
    5. Lwin, Khin T. & Qu, Rong & MacCarthy, Bart L., 2017. "Mean-VaR portfolio optimization: A nonparametric approach," European Journal of Operational Research, Elsevier, vol. 260(2), pages 751-766.
    6. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2017. "Portfolio optimization of safety measures for reducing risks in nuclear systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 20-29.
    7. Di Maio, Francesco & Picoco, Claudia & Zio, Enrico & Rychkov, Valentin, 2017. "Safety margin sensitivity analysis for model selection in nuclear power plant probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 122-138.

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