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Multi-Objective Optimum Design and Maintenance of Safety Systems: An In-Depth Comparison Study Including Encoding and Scheduling Aspects with NSGA-II

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  • Andrés Cacereño

    (Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI), Universidad de Las Palmas de Gran Canaria (ULPGC), Campus Universitario de Tafira Baja, 35017 Las Palmas de Gran Canaria, Spain)

  • David Greiner

    (Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI), Universidad de Las Palmas de Gran Canaria (ULPGC), Campus Universitario de Tafira Baja, 35017 Las Palmas de Gran Canaria, Spain)

  • Blas J. Galván

    (Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI), Universidad de Las Palmas de Gran Canaria (ULPGC), Campus Universitario de Tafira Baja, 35017 Las Palmas de Gran Canaria, Spain)

Abstract

Maximising profit is an important target for industries in a competitive world and it is possible to achieve this by improving the system availability. Engineers have employed many techniques to improve systems availability, such as adding redundant devices or scheduling maintenance strategies. However, the idea of using such techniques simultaneously has not received enough attention. The authors of the present paper recently studied the simultaneous optimisation of system design and maintenance strategy in order to achieve both maximum availability and minimum cost: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was coupled with Discrete Event Simulation in a real encoding environment in order to achieve a set of non-dominated solutions. In this work, that study is extended and a thorough exploration using the above-mentioned Multi-objective Evolutionary Algorithm is developed using an industrial case study, paying attention to the possible impact on solutions as a result of different encodings, parameter configurations and chromosome lengths, which affect the accuracy levels when scheduling preventive maintenance. Non-significant differences were observed in the experimental results, which raises interesting conclusions regarding flexibility in the preventive maintenance strategy.

Suggested Citation

  • Andrés Cacereño & David Greiner & Blas J. Galván, 2021. "Multi-Objective Optimum Design and Maintenance of Safety Systems: An In-Depth Comparison Study Including Encoding and Scheduling Aspects with NSGA-II," Mathematics, MDPI, vol. 9(15), pages 1-39, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:15:p:1751-:d:601077
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    References listed on IDEAS

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    1. Tian, Zhigang & Zuo, Ming J., 2006. "Redundancy allocation for multi-state systems using physical programming and genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1049-1056.
    2. Zhao, Jian-Hua & Liu, Zhaoheng & Dao, My-Thien, 2007. "Reliability optimization using multiobjective ant colony system approaches," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 109-120.
    3. Zhang, Chen & Yang, Tao, 2021. "Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ," Renewable Energy, Elsevier, vol. 164(C), pages 1540-1549.
    4. Bressi, Sara & Santos, João & Losa, Massimo, 2021. "Optimization of maintenance strategies for railway track-bed considering probabilistic degradation models and different reliability levels," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    5. Safari, Jalal, 2012. "Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 10-20.
    6. Kayedpour, Farjam & Amiri, Maghsoud & Rafizadeh, Mahmoud & Shahryari Nia, Arash, 2017. "Multi-objective redundancy allocation problem for a system with repairable components considering instantaneous availability and strategy selection," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 11-20.
    7. Adjoul, Oussama & Benfriha, Khaled & Zant, Chawki El & Aoussat, Améziane, 2021. "Algorithmic Strategy for Simultaneous Optimization of Design and Maintenance of Multi-Component Industrial Systems," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    8. Gao, Yicong & Feng, Yixiong & Zhang, Zixian & Tan, Jianrong, 2015. "An optimal dynamic interval preventive maintenance scheduling for series systems," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 19-30.
    9. Limbourg, Philipp & Kochs, Hans-Dieter, 2008. "Multi-objective optimization of generalized reliability design problems using feature models—A concept for early design stages," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 815-828.
    10. Hong-Zhong Huang & Jian Qu & Ming Zuo, 2009. "Genetic-algorithm-based optimal apportionment of reliability and redundancy under multiple objectives," IISE Transactions, Taylor & Francis Journals, vol. 41(4), pages 287-298.
    11. Sanchez, Ana & Carlos, Sofia & Martorell, Sebastian & Villanueva, Jose F., 2009. "Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 22-32.
    12. Khalili-Damghani, Kaveh & Abtahi, Amir-Reza & Tavana, Madjid, 2013. "A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 58-75.
    13. Salazar, Daniel & Rocco, Claudio M. & Galván, Blas J., 2006. "Optimization of constrained multiple-objective reliability problems using evolutionary algorithms," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1057-1070.
    14. Kumar, Ranjan & Izui, Kazuhiro & Yoshimura, Masataka & Nishiwaki, Shinji, 2009. "Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 891-904.
    15. Taboada, Heidi A. & Baheranwala, Fatema & Coit, David W. & Wattanapongsakorn, Naruemon, 2007. "Practical solutions for multi-objective optimization: An application to system reliability design problems," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 314-322.
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    2. Cheng, Dawei & Lu, Zhong & Zhou, Jia & Liang, Xihui, 2023. "An optimizing maintenance policy for airborne redundant systems operating with faults by using Markov process and NSGA-II," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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