IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v62y2025i1d10.1007_s12597-024-00780-2.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-024-00780-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12597-024-00780-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
    4. 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).
    5. 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).
    6. 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.
    7. 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).
    8. 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).
    9. 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.
    10. 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.
    11. 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).
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dui, Hongyan & Zhang, Songru & Dong, Xinghui & Wu, Shaomin, 2025. "Digital twin-enhanced opportunistic maintenance of smart microgrids based on the risk importance measure," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    2. Dui, Hongyan & Wang, Xinyue & Dong, Xinghui & Zhu, Tianmeng & Zhai, Yunkai, 2024. "Reliability model and emergency maintenance strategies for smart home systems," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    3. Deng, Qichen & Santos, Bruno F., 2022. "Lookahead approximate dynamic programming for stochastic aircraft maintenance check scheduling optimization," European Journal of Operational Research, Elsevier, vol. 299(3), pages 814-833.
    4. KarabaÄŸ, Oktay & Eruguz, Ayse Sena & Basten, Rob, 2020. "Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    5. Jiang, Junwei & An, Youjun & Dong, Yuanfa & Hu, Jiawen & Li, Yinghe & Zhao, Ziye, 2023. "Integrated optimization of non-permutation flow shop scheduling and maintenance planning with variable processing speed," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Sciau, Jean-Baptiste & Goyon, Agathe & Sarazin, Alexandre & Bascans, Jérémy & Prud’homme, Charles & Lorca, Xavier, 2024. "Using constraint programming to address the operational aircraft line maintenance scheduling problem," Journal of Air Transport Management, Elsevier, vol. 115(C).
    7. Liu, Gehui & Chen, Shaokuan & Ho, Tinkin & Ran, Xinchen & Mao, Baohua & Lan, Zhen, 2022. "Optimum opportunistic maintenance schedule over variable horizons considering multi-stage degradation and dynamic strategy," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Zhuang, Liangliang & Xu, Ancha & Wang, Yijun & Tang, Yincai, 2024. "Remaining useful life prediction for two-phase degradation model based on reparameterized inverse Gaussian process," European Journal of Operational Research, Elsevier, vol. 319(3), pages 877-890.
    9. Liu, Yu & Chen, Yiming & Jiang, Tao, 2020. "Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach," European Journal of Operational Research, Elsevier, vol. 283(1), pages 166-181.
    10. Anwar, Ghazanfar Ali & Zhang, Xiaoge, 2024. "Deep reinforcement learning for intelligent risk optimization of buildings under hazard," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    11. Merve Dogruel & Seniye Umit, 2021. "Prediction of Innovation Values of Countries Using Data Mining Decision Trees and a Comparative Application with Linear Regression Model," Istanbul Business Research, Istanbul University Business School, vol. 50(2), pages 465-493, November.
    12. Tseremoglou, Iordanis & Santos, Bruno F., 2024. "Condition-Based Maintenance scheduling of an aircraft fleet under partial observability: A Deep Reinforcement Learning approach," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    13. Kammouh, Omar & Fecarotti, Claudia & Marandi, Ahmadreza, 2024. "A scalable optimization approach to the intervention planning of complex interconnected infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    14. Li, Jiahui & Qi, Xiaogang & He, Yi & Liu, Lifang, 2024. "SDN candidate and protection path selection for link failure protection in hybrid SDNs," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    15. Zhang, Qin & Fang, Zhigeng & Cai, Jiajia, 2021. "Preventive replacement policies with multiple missions and maintenance triggering approaches," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    16. Xie, Haipeng & Tang, Lingfeng & Zhu, Hao & Cheng, Xiaofeng & Bie, Zhaohong, 2023. "Robustness assessment and enhancement of deep reinforcement learning-enabled load restoration for distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    17. Liu, Lujie & Yang, Jun & Kong, Xuefeng & Xiao, Yiyong, 2022. "Multi-mission selective maintenance and repairpersons assignment problem with stochastic durations," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    18. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2022. "A two-stage stochastic programming model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    19. Chen, Rentong & Zhang, Chao & Wang, Shaoping & Zio, Enrico & Dui, Hongyan & Zhang, Yadong, 2023. "Importance measures for critical components in complex system based on Copula Hierarchical Bayesian Network," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    20. Dui, Hongyan & Liu, Meng & Song, Jiaying & Wu, Shaomin, 2023. "Importance measure-based resilience management: Review, methodology and perspectives on maintenance," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:opsear:v:62:y:2025:i:1:d:10.1007_s12597-024-00780-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.