FUZZY SETS AND MULTI-CRITERIA DECISION MAKING: A BIBLIOMETRIC ANALYSIS

Year 2025, Volume: 29 Issue: 1, 66 - 86, 25.03.2025

Saadettin Aydın

https://doi.org/10.53487/atasobed.1635401

Abstract

Fuzzy set theory and Multi-Criteria Decision Making (MCDM) methods are increasingly gaining attention in academic literature. However, there is a need for a comprehensive bibliometric analysis on the academic development, key contributions and research trends of research in this field. This study aims to evaluate the development in this field by conducting a bibliometric analysis of academic research on MCDM and fuzzy set theory. It provides a framework to guide future research by determining publication trends, the most cited studies, the most active institutions and influential authors. A total of 3477 academic publications were examined using the bibliometric analysis method. The distribution of publications by year, citation analyses and keyword network analyses were conducted. There is an annual average publication volume of MCDM and fuzzy sets growing by 14.82%. The significant increase between 2020-2023 shows that the need for decision-making processes has increased in the post-pandemic period. The average 27.45 citations per article shows that the academic impact of studies in this field is high. Journal of Intelligent and Fuzzy Systems is one of the most published journals. The most influential publication is Yager RR’s study published in 2014, which received 2335 citations. Istanbul Technical University and universities in China stand out among the most active institutions. Kahraman C. stands out among the most influential authors. Keywords such as “Decision Making”, “Fuzzy Logic” and “Sensitivity Analysis” represent the basic research trends in this field. This analysis shows that the development of MCDM and fuzzy set theory in the academic field is rapid and continues to remain on the agenda. The study emphasizes the global importance of research in this field and provides a guiding resource for future academic studies.

Keywords

Multi-Criteria Decision Making, Fuzzy Sets, Bibliometric Analysis, Biblioshiny, VOSviewer

BULANIK KÜME VE ÇOK KRİTERLİ KARAR VERME: BİBLİYOMETRİK İNCELEME

Abstract

Bulanık küme teorisi ve Çok Kriterli Karar Verme (ÇKKV) yöntemleri akademik literatürde giderek daha fazla ilgi görmektedir. Ancak bu alandaki araştırmaların akademik gelişimi, anahtar katkılar ve araştırma eğilimleri konusunda kapsamlı bir bibliyometrik analiz ihtiyacı bulunmaktadır. Bu çalışma, ÇKKV ve bulanık küme teorisi üzerine yapılan akademik araştırmaların bibliyometrik analizini yaparak bu alandaki gelişimi değerlendirmeyi amaçlamaktadır. Yayın trendlerini, en çok atıf alan çalışmaları, en aktif kurumları ve etkili yazarları belirleyerek, gelecekteki araştırmalara rehberlik edecek bir çerçeve sunmaktadır. Bibliyometrik analiz yöntemi kullanılarak, toplam 3477 akademik yayın incelenmiştir. Yayınların yıllara göre dağılımı, atıf analizleri ve anahtar kelime ağ analizleri yapılmıştır. ÇKKV ve bulanık küme alanında yıllık ortalama %14,82 büyüyen bir yayın hacmi mevcuttur. 2020-2023 arasındaki belirgin artış, pandemi sonrası dönemde karar verme süreçlerine duyulan ihtiyacın arttığını göstermektedir. Makale başına ortalama 27,45 atıf alınması, bu alandaki çalışmaların akademik etkisinin yüksek olduğunu göstermektedir. Journal of Intelligent and Fuzzy Systems, en çok yayın yapılan dergilerden biridir. En etkili yayın, Yager RR’nin 2014 yılında yayımlanan ve 2335 atıf alan çalışmasıdır. En aktif kurumlar arasında İstanbul Teknik Üniversitesi ve Çin'deki üniversiteler öne çıkmaktadır. En etkili yazarlar arasında Kahraman C. dikkat çekmektedir. "Karar Verme", "Bulanık Mantık" ve "Duyarlılık Analizi" gibi anahtar kelimeler, bu alandaki temel araştırma eğilimlerini temsil etmektedir. Bu analiz, ÇKKV ve bulanık küme teorisinin akademik alandaki gelişiminin hızlı olduğunu ve gündemde kalmaya devam ettiğini göstermektedir. Çalışma, bu alanda yapılan araştırmaların küresel önemini vurgulamakta ve gelecekteki akademik çalışmalar için yol gösterici bir kaynak sunmaktadır.

Keywords

Çok Kriterli Karar Verme, Bulanık Küme, Bibliyometrik Analiz, Biblioshiny, VOSviewer

References

• Atanassov, K. (2016). Intuitionistic fuzzy sets. International Journal Bioautomation, 20, 1.
• Atanassov, K. T. (2017). Type-1 fuzzy sets and intuitionistic fuzzy sets.
• Algorithms, 10(3), 106. https://doi.org/10.3390/a10030106.
• Bhol, S.G. (2025). Applications of multi criteria decision making methods in cyber security. in: choudhury, A., Kaushik, K., Kumar, V., Singh, B.K. (eds) Cyber-Physical Systems Security. Studies in Big Data, vol 154. Springer, Singapore. https://doi.org/10.1007/978-981-97-5734-3_11.
• Blanco-Mesa, F., Merigó, J. M., & Gil-Lafuente, A. M. (2017). Fuzzy decision making: A bibliometric-based review. Journal of Intelligent & Fuzzy Systems, 32(3), 2033-2050. https://doi.org/10.3233/JIFS-161640.
• Castelló-Sirvent, F., Meneses-Eraso, C., Alonso-Gómez, J., & Peris-Ortiz, M. (2022). Three decades of fuzzy AHP: A bibliometric analysis. Axioms, 11(10), 525. https://doi.org/10.3390/axioms11100525.
• Chatterjee, P., & Chakraborty, S. (2014). Investigating the effect of normalization norms in flexible manufacturing sytem selection using multi-criteria decision-making methods. Journal of Engineering Science & Technology Review, 7(3). https://doi.org/10.25103/jestr.073.23.
• Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9. https://doi.org/10.1016/S0165-0114(97)00377-1.
• Demir, G., Chatterjee, P., & Pamučar, D. (2024a). Sensitivity analysis in multi-criteria decision-making: A state-of-the-art research perspective using bibliometric analysis. Expert Systems with Applications, 237, 121660. https://doi.org/10.1016/j.eswa.2023.121660.
• Demir, G., Chatterjee, P., Kadry, S., Abdelhadi, A., & Pamučar, D. (2024b). Measurement of alternatives and ranking according to Compromise Solution (MARCOS) Method: A Comprehensive bibliometric analysis. Decision Making: Applications in Management and Engineering, 7(2), 313-336. https://doi.org/10.31181/dmame7220241137
• Demir, G., Chatterjee, P., Zakeri, S., & Pamučar, D. (2024c). Mapping the evolution of multi- attributiveborder approximation area comparison (MABAC) method: A bibliometric analysis. Decision Making: Applications in Management and Engineering, 7(1) 290-314. https://doi.org/10.31181/dmame7120241037
• Kahraman, C., Kaya, İ., & Cebi, S. (2009). A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy, 34(10), 1603-1616. https://doi.org/10.1016/j.energy.2009.07.008
• Kumar, R. (2025). A Comprehensive review of MCDM methods, applications, and emerging trends. Decision Making Advances, 3(1), 185-199. https://doi.org/10.31181/dma31202569.
• Kumar, R., & Pamučar, D. (2025). A Comprehensive and systematic review of Multi-Criteria Decision-Making (MCDM) Methods to solve decision-making problems: Two decades from 2004 to 2024. Spectrum of Decision Making and Applications, 2(1), 178-197. https://doi.org/10.31181/sdmap21202524
• Liao, H., Yang, S., Kazimieras Zavadskas, E., & Škare, M. (2023). An overview of fuzzy multi-criteria decisionmaking methods in hospitality and tourism industries: bibliometrics, methodologies, applications and future directions. Economic research-Ekonomska istraživanja, 36(3). https://doi.org/10.1080/1331677X.2022.2150871
• Liu, W., & Liao, H. (2017). A bibliometric analysis of fuzzy decision research during 1970–2015. International Journal of Fuzzy Systems, 19, 1-14. https://doi.org/10.1007/s40815-016-0272-z
• Mendel, J. M. (2007). Type-2 fuzzy sets and systems: an overview. IEEE computational intelligence magazine, 2(1), 20-29. https://doi.org/10.1109/MCI.2007.380672
• Moore, R., & Lodwick, W. (2003). Interval analysis and fuzzy set theory. Fuzzy sets and systems, 135(1), 5-9. https://doi.org/10.1016/S0165-0114(02)00246-4
• Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445-455. https://doi.org/10.1016/S0377-2217(03)00020-1
• Pamučar, D., Stević, Ž., & Sremac, S. (2018). A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (FUCOM). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393
• Ramot, D., Milo, R., Friedman, M., & Kandel, A. (2002). Complex fuzzy sets. IEEE transactions on fuzzy systems, 10(2), 171-186. https://doi.org/10.1109/91.995119
• Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation: McGraw-Hill. New York.
• Sahoo, S. K., Choudhury, B. B., & Dhal, P. R. (2024). A bibliometric analysis of material selection using ÇKKV methods: trends and insights. Spectrum of Mechanical Engineering and Operational Research, 1(1), 189-205. https://doi.org/10.31181/smeor11202417
• Sahoo, S. K., Choudhury, B. B., Dhal, P. R., & Hanspal, M. S. (2025). A Comprehensive review of Multi-criteria Decision-making (MCDM) toward sustainable renewable energy development. Spectrum of Operational Research, 2(1), 268-284. https://doi.org/10.31181/sor21202527
• Triantaphyllou, E. (2000). Multi-Criteria decision making methods: A Comparative Study. Springer.
• Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
• Valdez, F., Castillo, O., & Melin, P. (2025). A Bibliometric Review of Type-3 Fuzzy Logic Applications. Mathematics, 13(3), 375. https://doi.org/10.3390/math13030375
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X