Sugeno–Weber based WASPAS in Fermatean neutrosophic structure

Handling vague, inconsistent, and incomplete information remains a significant challenge in multi-criteria group decision-making (MCGDM) under uncertainty. To address this issue, this study proposes a novel framework for MCGDM in uncertain environments. Specifically, we develop two novel aggregation operators, namely Fermatean neutrosophic Sugeno–Weber weighted averaging (FNSWWA) operator and Fermatean neutrosophic Sugeno–Weber weighted geometric (FNSWWG) operator, and investigate their properties in detail. These operators integrate the strong aggregation capabilities of the Sugeno–Weber t -norm and t -conorm with the expressive power of Fermatean neutrosophic sets, which can capture a broader spectrum of uncertainty. Furthermore, the proposed FNSW operators are incorporated into the WASPAS method to handle complex group decision-making problems. A numerical example involving the selection of a solar panel supplier is presented to demonstrate the applicability and effectiveness of the proposed approach. The results confirm that the FNSW operators and the extended WASPAS model provide robust, reliable, and computationally efficient solutions for MCGDM problems characterized by inherent uncertainty and incomplete information, offering valuable decision-support tools for practitioners in technology and renewable energy sectors.