Issue:A novel complex intuitionistic fuzzy AHP method and its application to electric vehicle selection

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Title of paper: A novel complex intuitionistic fuzzy AHP method and its application to electric vehicle selection Author(s): Cengiz Kahraman     0000-0001-6168-8185 Industrial Engineering Department, Istanbul Technical University, 34367 Macka, Istanbul, Türkiye kahramanc@itu.edu.tr Selcuk Cebi     0000-0001-9318-1135 Industrial Engineering Department, Yildiz Technical University, 34349 Yildiz, Istanbul, Türkiye scebi@yildiz.edu.tr Basar Oztaysi     0000-0002-1090-7963 Industrial Engineering Department, Istanbul Technical University, 34367 Macka, Istanbul, Türkiye oztaysib@itu.edu.tr Sezi Cevik Onar     0000-0001-6451-6709 Industrial Engineering Department, Istanbul Technical University, 34367 Macka, Istanbul, Türkiye cevikse@itu.edu.tr Seren Kadikoylu     0000-0003-0598-3934 Industrial Engineering Department, Istanbul Technical University, 34367 Macka, Istanbul, Türkiye kadikoylu20@itu.edu.tr Published in: Notes on Intuitionistic Fuzzy Sets, Volume 31 (2025), Number 4, pages 553–567 DOI: https://doi.org/10.7546/nifs.2025.31.4.553-567 Download:  PDF (1331  Kb, File info) Abstract: Multi-criteria decision-making (MCDM) methods often involve uncertainty and subjective judgment. The Analytic Hierarchy Process (AHP) has been widely used to structure and solve such problems, and its extension with intuitionistic fuzzy sets (IF-AHP) allows for modeling both membership and non-membership degrees. However, ordinary intuitionistic fuzzy sets may not fully capture complex uncertainties that include phase or directional information. This paper proposes a Complex Intuitionistic Fuzzy AHP (CIF-AHP) approach, integrating complex intuitionistic fuzzy sets into the traditional AHP framework. The methodology is applied to capture consumers’ purchasing preferences regarding electric vehicles (EVs) under uncertainty, and a comparative analysis is conducted between CIF-AHP and ordinary IF-AHP. The results demonstrate that CIF-AHP provides richer information and enhanced differentiation among alternatives, offering a more nuanced decision-making tool in uncertain environments. The contribution of this study to the literature lies in identifying and weighting the key factors influencing EV adoption through a method that explicitly accounts for vagueness and hesitation in decision-making. Keywords: Complex intuitionistic fuzzy sets, Analytic hierarchy process, AHP, Intuitionistic fuzzy AHP, Multi-criteria decision-making, MCDM. AMS Classification: 03E72, 90B50, 90C31. References: Alkouri, A. S., & Salleh, A. R. (2012). Complex intuitionistic fuzzy sets. AIP Conference Proceedings, 1482, 464–470. Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. Greenfield, S., Chiclana, F., & Dick, S. (2016). Interval-valued complex fuzzy logic. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, Canada, 2016, pp. 2014–2019. Ramot, D., Friedman, M., Langholz, G., & Kandel, A. (2003). Complex fuzzy logic. IEEE Transactions on Fuzzy Systems, 11(4), 450–461. In: Proceedings of the 10th International Fuzzy Systems Association World Congress. (pp. 1045–1049). Springer. https://doi.org/10.1007/978-3-540-45226-3_101. Ramot, D., Milo, R., Friedman, M., & Kandel, A. (2002). Complex fuzzy sets. IEEE Transactions on Fuzzy Systems, 10(2), 171–186. Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. Citations: The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.