Abstract

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.