(untitled) (European Journal of Pure and Applied Mathematics, 2025)
The Aczel-Alsina (AA) aggregation operators (AOs) are highly effective in optimizing complex decision-making (DM) tasks involving extensive datasets, particularly in the context of T-spherical fuzzy (T-SF) environments. This research creates a refined decision framework that integrates the AAT-norm (TN) and T-conorm (T-CN) operations with T-spherical fuzzy soft sets (T-SFSs). Two novel aggregation operators, named the T-SFS AA Weighted Geometric (T-SFSAAWG) and T-SFS AA Weighted Averaging (T-SFSAAWA) operators, are put forward. Their mathematical behavior, properties, and specific instances are thoroughly investigated. The Technique for Alternative Ordering of Variables (TAOV) is incorporated into the model to reinforce the ranking process. With the TAOV method, the time and computational effort required for ranking can be diminished, all while preserving precision and stability. Our proposed methodology is applied to a supplier selection problem, demonstrating its superior optimization effectiveness compared to current T-SFS and AA-based approaches. The model has been shown to yield consistent and efficient results through comparative and sensitivity analyses, establishing it as a practical and trustworthy resource for making data-informed decisions.