An Integrated MCDM Framework for Sensitivity of Weighting Methods and Ranking Aggregation: Evidence From Financial Performance Evaluation

Nazife Şahin Macit

doi:10.31181/dma412026196

Authors

Nazife Şahin Macit Department of Electrical and Energy, Kelkit Aydin Dogan Vocational School, Gumushane University, Gumushane, Turkey https://orcid.org/0000-0002-7996-4704

DOI:

https://doi.org/10.31181/dma412026196

Keywords:

Multi-Criteria Decision Making (MCDM), Financial performance evaluation, Criterion weighting, Weight sensitivity analysis, Ranking aggregation, Enropy, WENSLO, MPSI, MACAD, CURLIA

Abstract

This study proposes an integrated framework for evaluating alternatives in Multi-Criteria Decision Making (MCDM) by addressing weight sensitivity and ranking integration. The aim is to examine the effects of different criteria-weighting methods on ranking results and to develop a more consistent decision-making structure by integrating the resulting rankings. Financial performance indicators of Derimod Konfeksiyon A.Ş., listed on Borsa Istanbul (BIST), were evaluated using the Entropy, WENSLO, and MPSI weighting methods, while the MACAD method was employed to rank the alternatives. The analysis was initially conducted for the 2018–2024 period; however, identical rankings obtained across all weighting methods limited the assessment of weight sensitivity. Therefore, the analysis period was extended to 2015–2024, resulting in ranking variations that enabled a more meaningful comparison of the weighting methods. In the final stage, the resulting rankings were integrated into a single consensus ranking using the CURLIA method. The findings indicate that the proposed framework reduces inconsistencies arising from the use of different weighting methods and produces more stable and reliable performance evaluations. Consequently, the study contributes to the MCDM literature by presenting an integrated methodological framework and demonstrating its applicability to financial performance evaluation.

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References

Taherdoost, H., & Madanchian, M. (2023). Multi-criteria decision making (MCDM) methods and concepts. Encyclopedia, 3(1), 77-87. https://doi.org/10.3390/encyclopedia3010006

Singh, M., & Pant, M. (2021). A review of selected weighing methods in MCDM with a case study. International Journal of System Assurance Engineering and Management, 12(1), 126-144. https://doi.org/10.1007/s13198-020-01033-3

Ayan, B., Abacıoğlu, S., & Basilio, M. P. (2023). A comprehensive review of the novel weighting methods for multi-criteria decision-making. Information, 14(5), 285. https://doi.org/10.3390/info14050285

Avramova, T., Peneva, T., & Ivanov, A. (2025). Overview of existing multi-criteria decision-making (MCDM) methods used in industrial environments. Technologies, 13(10), 444. https://doi.org/10.3390/technologies13100444

Van Dua, T., Van Duc, D., Bao, N. C., & Trung, D. D. (2024). Integration of objective weighting methods for criteria and MCDM methods: application in material selection. EUREKA: Physics and Engineering, (2), 131-148. https://doi.org/10.21303/2461-4262.2024.003171

Odu, G. O. (2019). Weighting methods for multi-criteria decision making technique. Journal of Applied Sciences and Environmental Management, 23(8), 1449-1457. https://dx.doi.org/10.4314/jasem.v23i8.7

Chodha, V., Dubey, R., Kumar, R., Singh, S., & Kaur, S. (2022). Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques. Materials Today: Proceedings, 50, 709-715. https://doi.org/10.1016/j.matpr.2021.04.487

Wang, J., Setiawansyah, S., Ardiansah, T., Ulum, F., & Sumanto, S. (2026). Decision support system for determining strategic warehouse locations using a combination of the WENSLO weighting and RAWEC method. JUTI: Jurnal Ilmiah Teknologi Informasi, 24(1), 165-184. https://doi.org/10.12962/j24068535.v24i1.a1456

Akın, N. G. (2025). Measuring the performance of airport operations in Türkiye with the MPSI based MABAC method. Verimlilik Dergisi, 59(4), 803-826. https://doi.org/10.51551/verimlilik.1647363

Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., & Langović, Z. (2022). Novel hybrid MPSI–MARA decision-making model for support system selection in an underground mine. Systems, 10(6), 248. https://doi.org/10.3390/systems10060248

Keshtpour, A., & Chakrabortty, R. K. (2025). The selection of saltwater desalination technology using new measurement alternatives by a combination of angle and distance (MACAD) method: a case study. Environment Systems and Decisions, 45(3), 41. https://doi.org/10.1007/s10669-025-10034-1

Trung, D. D., Trang, B. T. T., Özçalici, M., & Ersoy, N. (2025). CURLIA: A streamlined aggregation method that elevates ranking accuracy in complex MCDM problems. Yugoslav Journal of Operations Research, (00), 40-40. https://doi.org/10.2298/YJOR250815040T

Paradowski, B., Shekhovtsov, A., Bączkiewicz, A., Kizielewicz, B., & Sałabun, W. (2021). Similarity analysis of methods for objective determination of weights in multi-criteria decision support systems. Symmetry, 13(10), 1874. https://doi.org/10.3390/sym13101874

Kizielewicz, B., Tomczyk, T., Gandor, M., & Sałabun, W. (2024). Subjective weight determination methods in multi-criteria decision-making: A systematic review. Procedia Computer Science, 246, 5396-5407. https://doi.org/10.1016/j.procs.2024.09.673

Li, M., Li, B., Chu, J., Wu, H., Yang, Z., Fan, J., Yang, L., Liu, P., & Long, J. (2023). Groundwater quality evaluation and analysis technology based on AHP-EWM-GRA and its application. Water, Air & Soil Pollution, 234, 19. https://doi.org/10.1007/s11270-022-06022-9

Mukhametzyanov, I. (2021). Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering, 4(2), 76-105. https://doi.org/10.31181/dmame210402076i

Baydaş, M., & Elma, O. E. (2021). An objectıve criteria proposal for the comparison of MCDM and weighting methods in financial performance measurement: An application in Borsa Istanbul. Decision Making: Applications in Management and Engineering, 4(2), 257-279. https://doi.org/10.31181/dmame210402257b

Saaty, R.W. (1987). The analytic hierarchy process: What it is and how it is used. Mathematical Modelling, 9(3-5), 161-176. https://doi.org/10.1016/0270-0255(87)90473-8

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009

Pamucar, D., Deveci, M., Gokasar, I., Işık, M., & Zizovic, M. (2021). Circular economy concepts in urban mobility alternatives using integrated DIBR method and fuzzy Dombi CoCoSo model. Journal of Cleaner Production, 323, 129096. https://doi.org/10.1016/j.jclepro.2021.129096

Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers and Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H

Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525. https://doi.org/10.3390/sym13040525

Zavadskas, E. K., & Podvezko, V. (2016). Integrated determination of objective criteria weights in MCDM. International Journal of Information Technology & Decision Making, 15(02), 267-283. https://doi.org/10.1142/S0219622016500036

Sharma, P., Ghatorha, K. S., Cepova, L., Ray, N. M., Kumar, A., Yadav, S. L., Schindlerova, V., & Phanden, R. K. (2025). A hybrid FAHP–Entropy–TOPSIS model for selecting the facility layout in small-scale manufacturing. Frontiers in Mechanical Engineering, 11, 1666571. https://doi.org/10.3389/fmech.2025.1666571

Wolny, M. (2025). Comparıson of key weighting methods in multi-criteria decision analysis. Scientific Papers of Silesian University of Technology. Organization and Management Series, (223). http://dx.doi.org/10.29119/1641-3466.2025.223.37

Shang, Z., Yang, X., Barnes, D., & Wu, C. (2022). Supplier selection in sustainable supply chains: Using the integrated BWM, fuzzy Shannon entropy, and fuzzy MULTIMOORA methods. Expert Systems with Applications, 195, 116567. https://doi.org/10.1016/j.eswa.2022.116567

Deepa, N., Ganesan, K., Srinivasan, K., & Chang, C. Y. (2019). Realizing sustainable development via modified integrated weighting MCDM model for ranking agrarian dataset. Sustainability, 11(21), 6060. https://doi.org/10.3390/su11216060

Srivastava, M. K., Gaur, S., & Ohri, A. (2024). Analysing the effectiveness of MCDM and integrated weighting approaches in groundwater quality index development. Water Conservation Science and Engineering, 9(2), 35. https://doi.org/10.1007/s41101-024-00267-7

Mohammadi, M., & Rezaei, J. (2020). Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods. Omega, 96, 102254. https://doi.org/10.1016/j.omega.2020.102254

Orakçı, E., & Özdemir, A. (2024). Using Social Choice Function for Multi Criteria Decision Making Problems. Alphanumeric Journal, 12(1), 21-38. https://doi.org/10.17093/alphanumeric.1426694

Arslan, R., & Bircan, H. (2020). Çok kriterli karar verme teknikleriyle elde edilen sonuçların Copeland yöntemiyle birleştirilmesi ve karşılaştırılması. Yönetim ve Ekonomi Dergisi, 27(1), 109-127. https://doi.org/10.18657/yonveek.540125

Khorasani Nejad, M., Rashidi, M., & Mousavi, V. (2025). Application of hybrid MCDA tools for constructability review in infrastructure projects: A bridge case study. Applied Sciences, 15(7), 3923. https://doi.org/10.3390/app15073923

Zavadskas, E. K., Cavallaro, F., Podvezko, V., Ubarte, I., & Kaklauskas, A. (2017). MCDM assessment of a healthy and safe built environment according to sustainable development principles: A practical neighborhood approach in Vilnius. Sustainability, 9(5), 702. https://doi.org/10.3390/su9050702

Filiz, E. (2025). Evaluation of NBA team performances with TOPSIS and BORDA counting methods. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 911-930. https://dergipark.org.tr/tr/pub/ckuiibfd/article/1657759

Aleksić, A. R., Delibašić, B. V., Jokić, Ž. M., & Radovanović, M. R. (2024). Application of the AHP and VIKOR methods of individual decision making and the Borda method of group decision making when choosing the most efficient way of performing preparatory shooting at serial number one from a 12.7 mm long range rifle M-93. Vojnotehnički Glasnik, 72(3), 1147-1170. https://doi.org/10.5937/vojtehg72-52204

Uluskan, M., Akpolat, G., & Şimşek, D. (2022). VAKIF üniversitelerinin AHP, COPRAS, SAW, TOPSIS yöntemleriyle değerlendirilmesi ve Borda sayım yöntemi ile bütünleşik bir sıra elde edilmesi. Endüstri Mühendisliği, 33(1), 22-61. https://doi.org/10.46465/endustrimuhendisligi.972512

Matejová, M., & Paralič, J. (2025). A multi-criteria decision-making approach for the selection of explainable AI methods. Machine Learning and Knowledge Extraction, 7(4), 158. https://doi.org/10.3390/make7040158

Rouhani Rad, S., Akhavan Anvari, M. R., & Raissifar, K. (2025). An integrated ranking model of Tehran stock exchange companies using Bayesian Best-Worst, CoCoSo, and MARCOS methods (Case Study: Food and Beverage Companies). International Journal of Finance & Managerial Accounting, 10(39), 55-88. https://www.ijfma.ir/article_23149.html

Erarslan, A., Kara, M. E., & Aktar Demirtas, E. (2025). Comparative analysis of hybrid MCDM methods for supplier evaluation: SWARA/AHP-based MOORA and WASPAS approach in the Turbocharger Industry. Operations Research Forum, 6(4), 184. https://doi.org/10.1007/s43069-025-00581-3

Chen, F., Bulgarova, B. A., & Kumar, R. (2025). Prioritizing generative artificial intelligence co-writing tools in newsrooms: A hybrid MCDM framework for transparency, stability, and editorial integrity. Mathematics, 13(23), 3791. https://doi.org/10.3390/math13233791

Sen, T., & Neogi, D. (2025). Evaluating the efficiency of Indian Cement Companies: An integration of MCDM and Copeland techniques. Asian Journal of Interdisciplinary Research, 8(4), 26-55. https://doi.org/10.54392/ajir2543

Kumar, V., & Pratihar, D. K. (2025). Biomechanical material selection for ankle-foot prosthetics: an ensemble MCDM-FEA framework. International Journal on Interactive Design and Manufacturing (IJIDeM), 19(12), 8839–8873. https://doi.org/10.1007/s12008-025-02340-4

Ma, H., Lim, K., Bao, Y., Liang, W., & Chen, T. (2025). Modeling purchase intentions in community-driven e-commerce platforms using hybrid MCDM and the SOR framework. International journal of research in industrial engineering, 14(4), 630-650. https://doi.org/10.22105/riej.2025.531183.1624

Çakrak, M., & Altuntaş, G. (2025). Measuring the corporate sustainability performance of selected airlines with entropy-based multi-criteria decision-making methods. International Journal of Multicriteria Decision Making, 10(2), 184-219. https://dx.doi.org/10.1504/IJMCDM.2025.150306

Shamsi, M., Mahmoudi, M. M., Rooholamini, H., & Motlagh, N. M. (2025). A hybrid fuzzy multi-criteria sustainability framework for incorporating recycled tire waste into green concrete technologies: Large scale applications of retaining walls and pavements. Construction and Building Materials, 500, 144126. https://doi.org/10.1016/j.conbuildmat.2025.144126

Salimi, N., Ferdosian, M., & Mahjouri, N. (2026). An adaptive agent-based framework for simulating farmers’ behavior in the face of drought: Application of numerical weather predictions. Water Resources Management, 40, 40. https://doi.org/10.1007/s11269-025-04451-9

Şimşek, A. B. (2024). Basitleştirilmiş bir envanter sınıflandırma yaklaşımı: Borda yöntemi ile çoklu kriterlerin birleştirilmesi. Üçüncü Sektör Sosyal Ekonomi Dergisi, 59(3), 1661-1678. http://dx.doi.org/10.15659/3.sektor-sosyal-ekonomi.24.09.2416

Feick, R. D., & Hall, G. B. (2001). Balancing consensus and conflict with a GIS-based multi-participant, multi-criteria decision support tool. GeoJournal, 53(4), 391-406. https://www.jstor.org/stable/41147627

Wang, C. N., Le, T. Q., Chang, K. H., & Dang, T. T. (2022). Measuring road transport sustainability using MCDM-based entropy objective weighting method. Symmetry, 14(5), 1033. https://doi.org/10.3390/sym14051033

El-Araby, A., Sabry, I., & El-Assal, A. (2022). A comparative study of using MCDM methods integrated with entropy weight method for evaluating facility location problem. Operational Research in Engineering Sciences: Theory and Applications, 5(1), 121-138. https://doi.org/10.31181/oresta250322151a

Zafar, S., Alamgir, Z., & Rehman, M. H. (2021). An effective blockchain evaluation system based on entropy-CRITIC weight method and MCDM techniques. Peer-to-Peer Networking and Applications, 14(5), 3110-3123. https://doi.org/10.1007/s12083-021-01173-8

Gezen Ucar, M. (2024). Integrated Entropy-based MCDM methods for investigating the effectiveness of Turkey’s energy policies. Energy Systems, 17(2), 717-746. https://doi.org/10.1007/s12667-024-00688-2

Wang, C. N., Nguyen, N. A. T., & Dang, T. T. (2023). Sustainable evaluation of major third-party logistics providers: a framework of an MCDM-based Entropy objective weighting method. Mathematics, 11(19), 4203. https://doi.org/10.3390/math11194203

Singh, H., & Mohanty, M. P. (2025). Multi-scale assessment and Entropy-MCDM framework for evaluating reanalysis precipitation datasets over Indian basins. International Journal of Applied Earth Observation and Geoinformation, 144, 104919. https://doi.org/10.1016/j.jag.2025.104919

Settu, K., & Jayalakshmi, M. (2025). Synergizing neutrosophic logic with Entropy-VIKOR of MCDM for superior AWR robot selection in manufacturing. Results in Engineering, 27, 106546. https://doi.org/10.1016/j.rineng.2025.106546

Dasgupta, A., Sen, B., Dutta, P., Rachchh, N., Patil, N., Mahapatro, A., & Karthikeyan, A. (2025). Entropy-TOPSIS-based material selection for sustainable polymer composite: An MCDM framework promoting circular economy. Journal of Elastomers & Plastics, 57(5), 703-729. https://doi.org/10.1177/00952443251331171

Mastilo, A. (2026). Reassessing national innovation performance through an Entropy CORASO framework: Evidence from the European innovation scoreboard. Smart Multi-Criteria Analytics and Reasoning Technologies, 2(1), 25-39. https://doi.org/10.65069/smart2120266

Popović, G., Karabašević, D., Stanujkić, D. (2026). A WENSLO-WEDBA Integrated Approach for Green Supplier Selection. In: Stević, Ž., Prentkovskis, O., Kostadinović, M., Danilevičius, A. (eds) NEW HORIZONS of Transport and Communications 2025. TransportaCom 2025. Lecture Notes in Intelligent Transportation and Infrastructure, 327-340. https://doi.org/10.1007/978-3-032-14078-4_30

Keleş, N., & Kahveci, A. (2025). Evaluating the logistics performance of the EU candidate and member countries using the WENSLO and ARTASI methods. Pamukkale University Journal of Social Sciences Institute, 68, 43-66. https://doi.org/10.30794/pausbed.1594714

Gürler, H. E., & Özçalıcı, M. (2025). Beyond equal weights: A WENSLO–CoCoSo assessment of OECD countries’ SDG performance. In Ş. Bayazit Bedirhanoğlu (Ed.), Nicel karar verme: Çok kriterli yaklaşımlar ve makine öğrenmesi uygulamaları (pp. 1-29). Özgür Yayınları. https://doi.org/10.58830/ozgur.pub788.c3305

Bayazit Bedirhanoğlu, Ş. (2025). Performance evaluation of sustainable universities with WENSLO-based AROMAN multi-criteria decision making method: Application with GreenMetric criteria. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 27(2), 369-390. https://doi.org/10.33707/akuiibfd.1683204

Aryanti, R., Wang, J., Wahyudi, A. D., Setiawansyah, S., & Darwis, D. (2025). Decision support system for selecting the best restaurant waiter using a combination of WENSLO weighting and AROMAN methods. JEECS (Journal of Electrical Engineering and Computer Sciences), 10(2), 128-141. https://doi.org/10.54732/jeecs.v10i2.4

Kaya, A., Gürler, H. E., Güngör Karyağdı, N., & Özçalıcı, M. (2025). Evaluating carbon and energy taxation performance in BRICS-T countries: An integrated WENSLO-MCRAT approach. Ekonomi Politika Ve Finans Araştırmaları Dergisi, 10(Özel Sayı), 235-251. https://doi.org/10.30784/epfad.1813740

Ozcalici, M., Ersoy, N., & Pamucar, D. (2027). Data-driven large-cap US stock price forecasting using a hybrid MCDM-machine learning approach. Spectrum of Operational Research, 3(1), 359-389. https://doi.org/10.31181/sor202771

Ersoy, N., Özçalıcı, M., & Trung, D. D. (2026). A hybrid MCDM approach for SDGs assessment of EU countries. Spectrum of Decision Making and Applications, 3(1), 164-186. https://doi.org/10.31181/sdmap31202637

Demir, E. (2025). Multi-criteria sustainability performance analysis of commercial banks in Türkiye based on MPSI and RAWEC methods. International Journal of Insurance and Finance, 5(1), 45-58. https://ijif.net/article/72

Akbulut, O. Y., & Aydın, Y. (2024). A hybrid multidimensional performance measurement model using the MSD-MPSI-RAWEC model for Turkish Banks. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(3), 1157-1183. https://doi.org/10.30798/makuiibf.1464469

Yalçın, G. C., & Edinsel, S. (2025). Comparative assessment of climate change performance: Türkiye vs. G7 countries using a hybrid MPSI-MABAC approach. Current Research in Social Sciences, 11(2), 438-456. https://doi.org/10.30613/curesosc.1627254

Şeyranlioğlu, O., Çilek, A., & Konuş, C. (2025). Financial performance and portfolio modelling in the BIST textile, apparel and leather sector with MPSI and RAPS MCDM methods. Finans Politik & Ekonomik Yorumlar, (672), 9-48. https://ekonomikyorumlar.com.tr/files/articles/1751231006.pdf

Kaya, N., Bozcuk, A. E., Ünal Uyar, G. F., Özden, M. S., Terzioğlu, M., Tutcu, B., & Talaş, H. (2026). Risk-sensitive performance evaluation of life insurance markets in EU and EEA countries: A MPSI–CoCoSo Approach. Risks, 14(4), 85. https://doi.org/10.3390/risks14040085

Gao, Z., Ding, L., Xiong, Q., Gong, Z., & Xiong, C. (2019). Image compressive sensing reconstruction based on z-score standardized group sparse representation. IEEE Access, 7, 90640-90651. https://doi.org/10.1109/ACCESS.2019.2927009

Zhang, X., Wang, C., Li, E., & Xu, C. (2014). Assessment model of ecoenvironmental vulnerability based on improved entropy weight method. The Scientific World Journal, 2014(1), 1-7. http://dx.doi.org/10.1155/2014/797814

Trivedi, R., & Dube, M. (2025). Modified TOPSIS model enhanced by shannon’s entropy weighting for evaluating agrarian advancement across multifarious Indian states. GeoJournal, 90(2), 75. https://doi.org/10.1007/s10708-025-11321-9

Wu, R. M. X., Zhang, Z., Yan, W., Fan, J., Gou, J., Liu, B., Gide, E., Soar, J., Shen, B., Fazal-e-Hasan, S., Liu, Z., Zhang, P., Wang, P., Cui, X., Peng, Z., & Wang, Y. (2022). A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement. PloS ONE, 17(1), e0262261. https://doi.org/10.1371/journal.pone.0262261

Pamucar, D., Ecer, F., Gligorić, Z., Gligorić, M., & Deveci, M. (2023). A novel WENSLO and ALWAS multicriteria methodology and its application to green growth performance evaluation. IEEE Transactions on Engineering Management, 71, 9510-9525. https://doi.org/10.1109/TEM.2023.3321697

Setiawansyah, S., Wang, J., Palupiningsih, P., & Maryana, S. (2026). Decision Support System for Evaluating Textile Supplier Performance Based on Weights by Envelope and Slope and Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria. Journal of Computer Science, Information Technology and Telecommunication Engineering, 7(1), 1079-1092. https://jurnal.umsu.ac.id/index.php/jcositte/article/view/29131

Akbulut, R., & Rençber, Ö. F. (2015). BİST’te İmalat Sektöründeki İşletmelerin Finansal Performansları Üzerine Bir Araştırma. Muhasebe Ve Finansman Dergisi, 65(65), 117-136. https://doi.org/10.25095/mufad.396520

Çelik, İ., & Ayan, S. (2017). Veri zarflama analizi ile imalat sanayi sektörünün finansal performans etkinliğinin ölçülmesi: Borsa İstanbul’da bir araştırma. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 8(18), 56-74. https://doi.org/10.21076/vizyoner.285998

Yanık, L., & Eren, T. (2017). Borsa İstanbul’da işlem gören otomotiv imalat sektörü firmalarının finansal performanslarının AHP, TOPSIS, ELECTRE ve VIKOR yöntemleri ile analizi. Yalova Sosyal Bilimler Dergisi, 7(13), 165-188. https://doi.org/10.17828/yalovasosbil.333899.

Şahin, A., & Bilgin Sarı, E. (2019). Entropi tabanli TOPSIS ve VIKOR yöntemleriyle Bist-imalat işletmelerinin finansal ve Borsa performanslarinin karşılaştırılması. Journal of Accounting and Taxation Studies, 12(2), 255-270. https://doi.org/10.29067/muvu.340678

Bardi, Ş. (2023). Gri ilişkisel analiz yöntemiyle finansal performans analizi: Bist imalat alt sektörler uygulaması. Sosyal Bilimler Akademi Dergisi, 6(2), 145-167. https://doi.org/10.38004/sobad.1333980

Public Disclosure Platform (KAP). (n.d.). Public Disclosure Platform. Available at: https://www.kap.org.tr/tr (Accessed: January 20, 2026)

Božanić, D., Puška, A., Tešić, D., Štilić, A., Ullah, K., Muhsen, Y. R., & Hezam, I. M. (2025). Fuzzy AHP-fuzzy MABAC model for ranking a combined construction machine-backhoe loader. Facta Universitatis, Series: Mechanical Engineering, 23(3), 605-625. https://doi.org/10.22190/FUME250801030B

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), 101-123. https://doi.org/10.22190/FUME230614026B

Nguyen, H. Q., Nguyen, V. T., Phan, D. P., Tran, Q. H., & Vu, N. P. (2022). Multi-criteria decision making in the PMEDM process by using MARCOS, TOPSIS, and MAIRCA methods. Applied Sciences, 12(8), 3720. https://doi.org/10.3390/app12083720

Le, H. A., Hoang, X. T., Trieu, Q. H., Pham, D. L., & Le, X. H. (2022). Determining the best dressing parameters for external cylindrical grinding using MABAC method. Applied Sciences, 12(16), 8287. https://doi.org/10.3390/app12168287