J. Ali - Institute of Numerical Sciences, Kohat University of Science and Technology, KPK, Kohat 26000, Pakistan. I.-L. Popa - Department of Computing, Mathematics and Electronics, “1 Decembrie 1918” University of Alba Iulia, 510009 Alba Iulia, Romania. - Faculty of Mathematics and Computer Science, Transilvania University of Brasov, Iuliu Maniu Street 50, 500091 Brasov, Romania. T. Anwar - School of Science, Walailak University, Nakhon Si Thammarat 80160, Thailand. - Research Center for Theoretical Simulation and Applied Research in Bioscience and Sensing, Walailak University, Nakhon Si Thammarat 80160, Thailand.
This paper introduces a novel cubic Fermatean fuzzy (CFF) Sugeno–Weber (SuW) aggregation-based method for multi-criteria group decision-making (MCGDM) under uncertainty. First, we define new SuW operational laws for CFF sets and investigate their key properties. Based on these, we develop several power aggregation operators (AOs) that effectively combine evaluation information in fuzzy environments. These AOs are then integrated into a novel MCGDM framework employing two types of maximizing deviation models to determine unknown criteria weights. The proposed method is validated through a real-world case study assessing project team member performance across multiple evaluation criteria. Furthermore, sensitivity analysis and comparative evaluation demonstrate the robustness and superiority of the proposed method over existing aggregation techniques, confirming its practical significance.
J. Ali, I.-L. Popa, T. Anwar, Cubic Fermatean fuzzy decision model for project team evaluation using Sugeno–Weber operators and maximizing deviation technique, Journal of Mathematics and Computer Science, 41 (2026), no. 3, 365--405
Ali J., Popa I.-L., Anwar T., Cubic Fermatean fuzzy decision model for project team evaluation using Sugeno–Weber operators and maximizing deviation technique. J Math Comput SCI-JM. (2026); 41(3):365--405
Ali, J., Popa, I.-L., Anwar, T.. "Cubic Fermatean fuzzy decision model for project team evaluation using Sugeno–Weber operators and maximizing deviation technique." Journal of Mathematics and Computer Science, 41, no. 3 (2026): 365--405
