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Application of dynamic maintenance strategy model based on group information and reliability

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  • Mohamad Javad Afzalinejad

    (Islamic Azad University)

Abstract

Experts in a variety of businesses have always considered choosing the right maintenance strategy to be a critical concern. Over the years, a variety of maintenance strategies have generally been offered; nevertheless, some researchers have taken steps to provide contingency strategies fit for the conditions as well as dynamic ones. The present research also aims to provide a proper maintenance strategy and compare it with other maintenance strategies from the perspective of experts. Therefore, first, the proposed method is fully described, and then, using the SWARA and COPRAS methods, a comparison is made between the proposed maintenance strategy and seven important maintenance strategies such as reliability-centered maintenance, condition-based maintenance, corrective maintenance, preventive maintenance, time-based maintenance, and failure-based maintenance. However, to determine the appropriate criteria, 13 criteria were extracted from the literature review, the important criteria included cost, time, reliability, etc. According to the expert population of this research including 10 maintenance experts in Tehran city and different industries, the ranking was done and finally, it was found that the proposed method had a noticeable superiority over other methods followed by reliability-centered maintenance and preventive maintenance.

Suggested Citation

  • Mohamad Javad Afzalinejad, 2025. "Application of dynamic maintenance strategy model based on group information and reliability," OPSEARCH, Springer;Operational Research Society of India, vol. 62(1), pages 55-76, March.
  • Handle: RePEc:spr:opsear:v:62:y:2025:i:1:d:10.1007_s12597-024-00780-2
    DOI: 10.1007/s12597-024-00780-2
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    References listed on IDEAS

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    1. Jin, Haibo & Song, Xianhe & Xia, Hao, 2023. "Optimal maintenance strategy for large-scale production systems under maintenance time uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    2. Zhang, Chao & Chen, Rentong & Wang, Shaoping & Dui, Hongyan & Zhang, Yadong, 2022. "Resilience efficiency importance measure for the selection of a component maintenance strategy to improve system performance recovery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Reshu Agarwal & Mandeep Mittal & Sarla Pareek, 2016. "Loss Profit Estimation Using Temporal Association Rule Mining," International Journal of Business Analytics (IJBAN), IGI Global, vol. 3(1), pages 45-57, January.
    4. Qinming Liu & Ming Dong & Wenyuan Lv & Chunming Ye, 2019. "Manufacturing system maintenance based on dynamic programming model with prognostics information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1155-1173, March.
    5. Nihan Çağlayan & Sina Abbasi & İbrahim Yilmaz & Babek Erdebilli & Tapan Senapati, 2024. "Bibliometric Analysis on the Distributed Decision, Decentralized Decision, and Fuzzy Logic," Discrete Dynamics in Nature and Society, Hindawi, vol. 2024, pages 1-13, January.
    6. Dao, Cuong D. & Zuo, Ming J., 2017. "Selective maintenance of multi-state systems with structural dependence," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 184-195.
    7. Wu, Tianyi & Yang, Li & Ma, Xiaobing & Zhang, Zihan & Zhao, Yu, 2020. "Dynamic maintenance strategy with iteratively updated group information," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    8. Andriotis, C.P. & Papakonstantinou, K.G., 2019. "Managing engineering systems with large state and action spaces through deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Suyog S. Patil & Anand K. Bewoor & Ravinder Kumar & Mohammad Hossein Ahmadi & Mohsen Sharifpur & Seepana PraveenKumar, 2022. "Development of Optimized Maintenance Program for a Steam Boiler System Using Reliability-Centered Maintenance Approach," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    10. Chaabane, K. & Khatab, A. & Diallo, C. & Aghezzaf, E.-H. & Venkatadri, U., 2020. "Integrated imperfect multimission selective maintenance and repairpersons assignment problem," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    11. Sanjib Biswas & Dragan Pamucar, 2023. "A modified EDAS model for comparison of mobile wallet service providers in India," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-31, December.
    12. Dui, Hongyan & Zhang, Yulu & Bai, Guanghan, 2024. "Analysis of variable system cost and maintenance strategy in life cycle considering different failure modes," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    13. Shaukat, Syed & Katscher, Mathias & Wu, Cheng-Lung & Delgado, Felipe & Larrain, Homero, 2020. "Aircraft line maintenance scheduling and optimisation," Journal of Air Transport Management, Elsevier, vol. 89(C).
    Full references (including those not matched with items on IDEAS)

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