IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v8y2017i2d10.1007_s13198-016-0554-8.html
   My bibliography  Save this article

A fuzzy-grey-weighted aggregate sum product assessment methodical approach for multi-criteria analysis of maintenance performance systems

Author

Listed:
  • D. E. Ighravwe

    (University of Lagos
    Ladoke Akintola University of Technology)

  • S. A. Oke

    (University of Lagos
    Covenant University)

Abstract

Diverse maintenance performance models have been previously proposed in literature. However, many of these frameworks perform inefficiently or are not applicable in real-world problems due to their over-simplified assumptions. Such models do not take into account peculiarities of the maintenance situation in which multiple factors need to be prioritised under uncertain conditions. Keeping the above issues in mind, this communication proposes a framework for ranking maintenance performance systems using integrated fuzzy entropy weighting method, grey relational analysis (GRA) and weighted aggregate sum product assessment (WASPAS). The values of criteria weights were determined using fuzz entropy weighting method. Ranking was carried out using GRA and WASPAS methods. GRA ranking considered a criterion, while WASPAS method considered multi-criteria. It is the belief of the authors that merging these three mentioned tools generates synergy. The synergic advantage of the fusion is that these tools interact to create the combined results of ability to handle logic decisions, or partial information and choice among complex alternatives, demonstrated in this paper. The built-up frame-work was illustrated with practical data from five manufacturing companies operating in Nigeria with information gathered through the questionnaire approach to show that the approach can be effectively implemented in practice. Based on the proposed framework’s results, the highest ranked maintenance system belongs to companies 4 and 5, while the lowest ranked maintenance system belongs to company 5. TOPSIS method was used to determine the best performing maintenance function of the companies. It was observed that maintenance system of company 4 was the highest ranked system. The results from model testing confirmed that the presented scheme is feasibility in industrial settings, efficient and capable of revealing the best company in performance according to certain six input criteria. The novelty of this approach is its uniqueness of the combined frameworks’ structures in achieving the highest accuracy of estimation, introduced for the first time in maintenance performance assessment in a multi-criteria framework.

Suggested Citation

  • D. E. Ighravwe & S. A. Oke, 2017. "A fuzzy-grey-weighted aggregate sum product assessment methodical approach for multi-criteria analysis of maintenance performance systems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 961-973, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0554-8
    DOI: 10.1007/s13198-016-0554-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-016-0554-8
    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/s13198-016-0554-8?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. Shima Lashgari & Jurgita Antuchevičienė & Alireza Delavari & Omid Kheirkhah, 2014. "Using QSPM and WASPAS methods for determining outsourcing strategies," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 15(4), pages 729-743, September.
    2. Zhou, Dengji & Zhang, Huisheng & Weng, Shilie, 2014. "A novel prognostic model of performance degradation trend for power machinery maintenance," Energy, Elsevier, vol. 78(C), pages 740-746.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Priyank Srivastava & Dinesh Khanduja & V. P. Agrawal, 2020. "Agile maintenance attribute coding and evaluation based decision making in sugar manufacturing plant," OPSEARCH, Springer;Operational Research Society of India, vol. 57(2), pages 553-583, June.
    2. Chia-Nan Wang & Yih-Tzoo Chen & Chun-Chun Tung, 2021. "Evaluation of Wave Energy Location by Using an Integrated MCDM Approach," Energies, MDPI, vol. 14(7), pages 1-14, March.
    3. Dilbagh Panchal & Priyank Srivastava, 2019. "Qualitative analysis of CNG dispensing system using fuzzy FMEA–GRA integrated approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(1), pages 44-56, February.

    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. Zhou, Dengji & Yu, Ziqiang & Zhang, Huisheng & Weng, Shilie, 2016. "A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation," Energy, Elsevier, vol. 109(C), pages 420-429.
    2. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    3. Yu, Weichao & Song, Shangfei & Li, Yichen & Min, Yuan & Huang, Weihe & Wen, Kai & Gong, Jing, 2018. "Gas supply reliability assessment of natural gas transmission pipeline systems," Energy, Elsevier, vol. 162(C), pages 853-870.
    4. Bakır Mahmut & Akan Şahap & Durmaz Emrah, 2019. "Exploring service quality of low-cost airlines in Europe: An integrated MCDM approach," Economics and Business Review, Sciendo, vol. 5(2), pages 109-130, June.
    5. Vazquez, Luis & Blanco, Jesús María & Ramis, Rolando & Peña, Francisco & Diaz, David, 2015. "Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring," Energy, Elsevier, vol. 93(P1), pages 923-944.
    6. Tsoutsanis, Elias & Meskin, Nader, 2017. "Derivative-driven window-based regression method for gas turbine performance prognostics," Energy, Elsevier, vol. 128(C), pages 302-311.
    7. Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    8. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2016. "A dynamic prognosis scheme for flexible operation of gas turbines," Applied Energy, Elsevier, vol. 164(C), pages 686-701.
    9. Ayşegül Tuş & Esra Aytaç Adalı, 2019. "The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem," OPSEARCH, Springer;Operational Research Society of India, vol. 56(2), pages 528-538, June.
    10. Esfandi, Saeed & Nourian, Farshad, 2021. "Urban carrying capacity assessment framework for mega mall development. A case study of Tehran’s 22 municipal districts," Land Use Policy, Elsevier, vol. 109(C).
    11. Weichao Yu & Xianbin Zheng & Weihe Huang & Qingwen Cai & Jie Guo & Jili Xu & Yang Liu & Jing Gong & Hong Yang, 2022. "A Data-Driven Methodology for the Reliability Analysis of the Natural Gas Compressor Unit Considering Multiple Failure Modes," Energies, MDPI, vol. 15(10), pages 1-18, May.
    12. Kiaee, Mehrdad & Tousi, A.M., 2021. "Vector-based deterioration index for gas turbine gas-path prognostics modeling framework," Energy, Elsevier, vol. 216(C).
    13. Pınar Miç & Z. Figen Antmen, 2021. "A Decision-Making Model Based on TOPSIS, WASPAS, and MULTIMOORA Methods for University Location Selection Problem," SAGE Open, , vol. 11(3), pages 21582440211, August.
    14. Zafer DURAN, 2023. "Evaluation of Supply Chain Resilience in N-11 Countries by MEREC Based EDAS, MARCOS, WASPAS Integrated Method," Yildiz Social Science Review, Yildiz Technical University, vol. 9(1), pages 1-15.

    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:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0554-8. 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.