Multi-attribute approach for enhancing maintenance processes in maintenance depots under a type-2 fuzzy environment
Keywords:
key performance indicators, operational management, maintenance process reliability, interval type-2 fuzzy numbers, Taxonomy method, group decision-making
Abstract
Introduction/purpose: The purpose of this research is to determine the priority of Key Performance Indicators (KPIs) in a precise and structured manner. By applying the fuzzy multi-attribute decision-making model, operational management can identify and prioritize activities that will enhance maintenance process reliability in the shortest possible time while simultaneously reducing costs.
Methods: The relative importance of sub-processes and KPI values is represented using predefined linguistic terms modelled by interval type-2 fuzzy numbers (IT2FNs). These assessments are formulated as a fuzzy group decision-making framework. The weight vector is determined using the fuzzy geometric mean, while the ranking of KPIs is obtained through the Taxonomy method combined with IT2FNs, which represents the main scientific contribution of this research.
Results: Real-world data gathered from a maintenance depot were used to test the proposed model. The study effectively modelled uncertainty in KPI evaluations using seven predefined linguistic expressions mapped onto IT2FNs. A consistent weight vector was obtained using the fuzzy group decision-making approach. Effective KPI ranking was achieved through a combination of the Taxonomy method and IT2FNs, which helped pinpoint the most important areas for operational improvement. The method's ability to provide clear priorities to support reliability improvements while cutting costs was validated through its application.
Conclusion: The key contributions of this study are: (i) fuzzy algebra rules with IT2FNs are used to determine the group utility value, and (ii) the integration of the Taxonomy method with IT2FNs for an improved decision-making procedure.
References
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Mardani, A., Jusoh, A. & Zavadskas, E. K. (2015).Fuzzy Multiple Criteria Decision-Making Techniques and Applications – Two Decades Review from 1994 to 2014.Expert Systems with Applications, 42(8), 4126–4148.
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Oğuz, A. (2024). Drone Selection for Agricultural Enterprises with Interval Type-2 Fuzzy COPRAS Method. Journal of Research in Business, 9(2), 395–421.
Palczewski, K. & Sałabun, W. (2019).The Fuzzy TOPSIS Applications in the Last Decade.Procedia Computer Science, 159, 2294–2303.
Peng, X. & Yang, Y. (2016). Fundamental Properties of Interval-Valued Pythagorean Fuzzy Aggregation Operators: Interval-Valued Pythagorean Fuzzy Aggregation Operations. International Journal of Intelligent Systems, 31(5), 444–487.
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Sudžum, R., Nestić, S., Komatina, N. & Kraišnik, M. (2024). An Intuitionistic Fuzzy Multi-Criteria Approach for Prioritizing Failures That Cause Overproduction: A Case Study in Process Manufacturing. Axioms, 13(6), 357.
Szmidt, E. & Kacprzyk, J. (2000).Distances between Intuitionistic Fuzzy Sets.Fuzzy Sets and Systems, 114(3), 505–518.
Tadić, D. & Komatina, N. (2025).A Hybrid Interval Type-2 Fuzzy DEMATEL-MABAC Approach for Strategic Failure Management in Automotive Manufacturing.Journal of Engineering Management and Systems Engineering, 4(1), 21–38.
Tadić, D., Lukić, J., Komatina, N., Marinković, D. & Pamučar, D. (2025).A Fuzzy Decision-Making Approach to Electric Vehicle Evaluation and Ranking.Tehnicki vjesnik/Technical Gazette, 4(32), accepted for publication.
Tian, Z., Wang, J. & Zhang, H. (2018). An Integrated Approach for Failure Mode and Effects Analysis Based on Fuzzy Best-Worst, Relative Entropy, and VIKOR Methods. Applied Soft Computing, 72, 636–646.
Toptancı, Ş., Gündoğdu, H. G., Korucuk, S., Aytekin, A. & Stević, Ž. (2023). Corporate Sustainability Strategy Selection for a Metropolitan Municipality Using a Trapezoidal Interval Type-2 Fuzzy SWARA–COPRAS Framework.Soft Computing. Retrieved February 14, 2025, from https://link.springer.com/10.1007/s00500-023-08800-x
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Zadeh, L. A. (1988). Fuzzy Logic.Computer, 21(4), 83–93.
Zhang, X. & Xu, Z. (2014). Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets: Extension of TOPSIS to Multiple Criteria Decision Making. International Journal of Intelligent Systems, 29(12), 1061–1078.
Zhong, L. & Yao, L. (2017).An ELECTRE I-Based Multi-Criteria Group Decision Making Method with Interval Type-2 Fuzzy Numbers and Its Application to Supplier Selection.Applied Soft Computing, 57, 556–576.
Aleksic, A., Runic Ristic, M., Komatina, N. & Tadic, D. (2019).Advanced Risk Assessment in Reverse Supply Chain Processes: A Case Study in Republic of Serbia.Advances in Production Engineering & Management, 14(4), 421–434.
Aleksić, A. & Tadić, D. (2023). Industrial and Management Applications of Type-2 Multi-Attribute Decision-Making Techniques Extended with Type-2 Fuzzy Sets from 2013 to 2022. Mathematics, 11(10), 2249.
Atanassov, K. T. (1999). Intuitionistic Fuzzy Sets, in: Intuitionistic Fuzzy Sets, (pp. 1–137). Studies in Fuzziness and Soft Computing. Heidelberg: Physica-Verlag HD. Retrieved March 30, 2025, from http://link.springer.com/10.1007/978-3-7908-1870-3_1
Božanić, D., Epler, I., Puška, A., Biswas, S., Marinković, D. & Koprivica, S. (2024). Application of the DIBR II – Rough MABAC Decision-Making Model for Ranking Methods and Techniques of Lean Organization Systems Management in the Process of Technical Maintenance.Facta Universitatis, Series: Mechanical Engineering, 22(1), 101.
Celik, E., Gul, M., Aydin, N., Gumus, A. T. & Guneri, A. F. (2015). A Comprehensive Review of Multi Criteria Decision Making Approaches Based on Interval Type-2 Fuzzy Sets. Knowledge-Based Systems, 85, 329–341.
Celik, E., Yucesan, M. & Gul, M. (2021). Green Supplier Selection for Textile Industry: A Case Study Using BWM-TODIM Integration under Interval Type-2 Fuzzy Sets. Environmental Science and Pollution Research, 28(45), 64793–64817.
Centobelli, P., Cerchione, R. & Esposito, E. (2018). How to Deal with Knowledge Management Misalignment: A Taxonomy Based on a 3D Fuzzy Methodology. Journal of Knowledge Management, 22(3), 538–566.
Diao, F., Cai, Q. & Wei, G. (2022). Taxonomy Method for Multiple Attribute Group Decision Making Under the Spherical Fuzzy Environment.Informatica, 713–729.
Ðurić, G., Mitrović, Č., Komatina, N., Tadić, D. & Vorotović, G. (2019).The Hybrid MCDM Model with the Interval Type-2 Fuzzy Sets for the Software Failure Analysis.Journal of Intelligent & Fuzzy Systems, 37(6), 7747–7759.
Heidarzade, A., Mahdavi, I. & Mahdavi-Amiri, N. (2016). Supplier Selection Using a Clustering Method Based on a New Distance for Interval Type-2 Fuzzy Sets: A Case Study. Applied Soft Computing, 38, 213–231.
Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z. & Antucheviciene, J. (2017). A New Multi-Criteria Model Based on Interval Type-2 Fuzzy Sets and EDAS Method for Supplier Evaluation and Order Allocation with Environmental Considerations. Computers & Industrial Engineering, 112, 156–174.
Komatina, N. (2025). A Novel BWM-RADAR Approach for Multi-Attribute Selection of Equipment in the Automotive Industry.Spectrum of Mechanical Engineering and Operational Research, 2(1), 104–120.
Komatina, N., Marinković, D., Tadić, D. & Pamučar, D. (2025). Advancing PFMEA Decision-Making: FRADAR Based Prioritization of Failure Modes Using AP, RPN, and Multi-Attribute Assessment in the Automotive Industry. Tehnički glasnik/Technical Journal, 19(3), accepted for publication.
Komatina, N., Tadić, D., Aleksić, A. & Banduka, N. (2022). The Integrated PFMEA Approach with Interval Type-2 Fuzzy Sets and FBWM: A Case Study in the Automotive Industry. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 236(6), 1201–1212.
Komatina, N., Tadić, D., Aleksić, A. & Jovanović, A. D. (2023).The Assessment and Selection of Suppliers Using AHP and MABAC with Type-2 Fuzzy Numbers in Automotive Industry.Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 237(4), 836–852.
Kutlu Gündoğdu, F. & Kahraman, C. (2019).Spherical Fuzzy Sets and Spherical Fuzzy TOPSIS Method.Journal of Intelligent & Fuzzy Systems, 36(1), 337–352.
Li, H., Guo, Y., Li, F., Cao, Y., Wang, L. & Ma, Y. (2022). Assessment of Operation Safety Risk for South-to-North Water Diversion Project: A Fuzzy VIKOR-FMEA Approach. Water Supply, 22(4), 3685–3701.
Liu, P. & Wang, D. (2022).An Extended Taxonomy Method Based on Normal T-Spherical Fuzzy Numbers for Multiple-Attribute Decision-Making.International Journal of Fuzzy Systems, 24(1), 73–90.
Mahmood, T., Ullah, K., Khan, Q. & Jan, N. (2019).An Approach toward Decision-Making and Medical Diagnosis Problems Using the Concept of Spherical Fuzzy Sets.Neural Computing and Applications, 31(11), 7041–7053.
Mardani, A., Jusoh, A. & Zavadskas, E. K. (2015).Fuzzy Multiple Criteria Decision-Making Techniques and Applications – Two Decades Review from 1994 to 2014.Expert Systems with Applications, 42(8), 4126–4148.
Mendel, J. M. (2017). Uncertain Rule-Based Fuzzy Systems: Introduction and New Directions, 2nd Edition. Cham: Springer International Publishing. Retrieved March 28, 2024, from http://link.springer.com/10.1007/978-3-319-51370-6
Mendel, J. M. & John, R. I. B. (2002). Type-2 Fuzzy Sets Made Simple. IEEE Transactions on Fuzzy Systems, 10(2), 117–127.
Milovanović, V., Aleksić, A., Milenkov, M. & Sokolović, V. (2024).Ranking of Asset Maintenance Process KPIs Using Fermatean Fuzzy Delphi and Fermatean Fuzzy ELECTRE Method.Journal of Intelligent & Fuzzy Systems, 1–17.
Nehi, H. M. & Keikha, A. (2016).TOPSIS and Choquet Integral Hybrid Technique for Solving MAGDM Problems with Interval Type-2 Fuzzy Numbers.Journal of Intelligent & Fuzzy Systems, 30(3), 1301–1310.
Oğuz, A. (2024). Drone Selection for Agricultural Enterprises with Interval Type-2 Fuzzy COPRAS Method. Journal of Research in Business, 9(2), 395–421.
Palczewski, K. & Sałabun, W. (2019).The Fuzzy TOPSIS Applications in the Last Decade.Procedia Computer Science, 159, 2294–2303.
Peng, X. & Yang, Y. (2016). Fundamental Properties of Interval-Valued Pythagorean Fuzzy Aggregation Operators: Interval-Valued Pythagorean Fuzzy Aggregation Operations. International Journal of Intelligent Systems, 31(5), 444–487.
Petrović, T., Vesić Vasović, J., Komatina, N., Tadić, D., Klipa, Đ. & Đurić, G. (2022).A Two-Stage Model Based on EFQM, FBWM, and FMOORA for Business Excellence Evaluation in the Process of Manufacturing.Axioms, 11(12), 704.
Sudžum, R., Nestić, S., Komatina, N. & Kraišnik, M. (2024). An Intuitionistic Fuzzy Multi-Criteria Approach for Prioritizing Failures That Cause Overproduction: A Case Study in Process Manufacturing. Axioms, 13(6), 357.
Szmidt, E. & Kacprzyk, J. (2000).Distances between Intuitionistic Fuzzy Sets.Fuzzy Sets and Systems, 114(3), 505–518.
Tadić, D. & Komatina, N. (2025).A Hybrid Interval Type-2 Fuzzy DEMATEL-MABAC Approach for Strategic Failure Management in Automotive Manufacturing.Journal of Engineering Management and Systems Engineering, 4(1), 21–38.
Tadić, D., Lukić, J., Komatina, N., Marinković, D. & Pamučar, D. (2025).A Fuzzy Decision-Making Approach to Electric Vehicle Evaluation and Ranking.Tehnicki vjesnik/Technical Gazette, 4(32), accepted for publication.
Tian, Z., Wang, J. & Zhang, H. (2018). An Integrated Approach for Failure Mode and Effects Analysis Based on Fuzzy Best-Worst, Relative Entropy, and VIKOR Methods. Applied Soft Computing, 72, 636–646.
Toptancı, Ş., Gündoğdu, H. G., Korucuk, S., Aytekin, A. & Stević, Ž. (2023). Corporate Sustainability Strategy Selection for a Metropolitan Municipality Using a Trapezoidal Interval Type-2 Fuzzy SWARA–COPRAS Framework.Soft Computing. Retrieved February 14, 2025, from https://link.springer.com/10.1007/s00500-023-08800-x
Wei, G., Tang, Y., Zhao, M., Lin, R. & Wu, J. (2020).Selecting the Low-Carbon Tourism Destination: Based on Pythagorean Fuzzy Taxonomy Method.Mathematics, 8(5), 832.
Xu, D.-Y., Cui, H.-Q., Wang, T.-Y., Fu, J.-W.& Zhu, G.-B. (2024). Interactions and Influences on Coal Miners’ Safety Attention: An Evaluation Using an Improved DEMATEL-ISM Approach. Facta Universitatis, Series: Mechanical Engineering, 22(1), 139.
Zadeh, L. A. (1988). Fuzzy Logic.Computer, 21(4), 83–93.
Zhang, X. & Xu, Z. (2014). Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets: Extension of TOPSIS to Multiple Criteria Decision Making. International Journal of Intelligent Systems, 29(12), 1061–1078.
Zhong, L. & Yao, L. (2017).An ELECTRE I-Based Multi-Criteria Group Decision Making Method with Interval Type-2 Fuzzy Numbers and Its Application to Supplier Selection.Applied Soft Computing, 57, 556–576.
Published
2026/01/22
Section
Original Scientific Papers
Copyright (c) 2026 Vladimir Milovanović, Aleksandar Aleksić, Snežana Nestić, Nikola Komatina, Vlada Sokolović, Marjan Milenkov
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