Višeatributivni pristup za unapređenje procesa održavanja u remontnim zavodima u fazi okruženju tip-2
Ključne reči:
ključni indikatori performansi, operativni menadžment, pouzdanost procesa održavanja, intervalni fazi brojevi tipa-2, metoda Taksonomije, grupno odlučivanje
Sažetak
Uvod / cilj rada: Cilj ovog istraživanja je određivanje prioriteta ključnih indikatora performansi (eng. KPI) na precizan i strukturiran način. Primenom modela odlučivanja koji se zasniva na višeatributivnoj fazi logici, operativni menadžment može identifikovati i prioritetno tretirati aktivnosti koje će u najkraćem mogućem roku poboljšati pouzdanost procesa održavanja, uz istovremeno smanjenje troškova.
Metode: Relativna važnost potprocesa i vrednosti KPI procenjivana je korišćenjem unapred definisanih lingvističkih iskaza modelovanih pomoću intervalnih fazi brojeva tipa-2 (eng. IT2FNs). Ove procene su formulisane kroz okvir grupnog odlučivanja u fazi okruženju. Vektori težina određeni su pomoću fazi geometrijske sredine, dok je rangiranje KPI izvršeno primenom metode Taksonomije u kombinaciji sa IT2FNs, što predstavlja glavni naučni doprinos ovog istraživanja.
Rezultati: Realni podaci prikupljeni iz jednog remontnog zavoda korišćeni su za testiranje predloženog modela. U istraživanju je uspešno modelovana neizvesnost u oceni KPI korišćenjem sedam unapred definisanih lingvističkih izraza mapiranih pomoću IT2FNs. Konzistentan vektor težina dobijen je kroz pristup fazi grupnog odlučivanja. Efikasno rangiranje KPI postignuto je kombinovanjem metode Taksonomije i IT2FNs, što je omogućilo identifikaciju najvažnijih oblasti za operativno unapređenje. Validacijom primene potvrđena je sposobnost metode da pruži jasne prioritete za unapređenje pouzdanosti uz smanjenje troškova.
Zaključak: Ključni doprinosi ove studije su: (i) upotreba pravila fazi algebre sa IT2FNs za određivanje grupne korisnosti, i (ii) integracija metode Taksonomije sa IT2FNs u cilju unapređenja procesa odlučivanja.
Reference
Aleksić, A., Milanović, D. D., Komatina, N. & Tadić, D. (2023).Evaluation and Ranking of Failures in Manufacturing Process by Combining Best‐worst Method and VIKOR under Type‐2 Fuzzy Environment.Expert Systems, 40(2), e13148.
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.
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.
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2026/01/22
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Originalni naučni radovi
Sva prava zadržana (c) 2026 Vladimir Milovanović, Aleksandar Aleksić, Snežana Nestić, Nikola Komatina, Vlada Sokolović, Marjan Milenkov
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