An Interval-Valued T-Spherical Fuzzy SWARA Approach with Sugeno–Weber Operators for Artificial Intelligence Selection

Rashid Kakis; Szabolcs Fischer; Dmytro Kurhan

doi:10.67334/cds21202624

Authors

Rashid Kakis Department of Mathematics, Riphah International University Lahore, Lahore, Pakistan Author https://orcid.org/0009-0004-1377-0503
Szabolcs Fischer Department of Transport Infrastructure and Water Resources Engineering, Faculty of Civil Engineering, Széchenyi István University, Hungary Author https://orcid.org/0000-0001-7298-9960
Dmytro Kurhan Department of Transport Infrastructure, Ukrainian State University of Science and Technologies, Ukraine Author https://orcid.org/0000-0002-9448-5269

DOI:

https://doi.org/10.67334/cds21202624

Keywords:

T-spherical fuzzy set, Sugeno–Weber, T-SFS, Multi-Attribute Decision-Making, MADM, Artificial Intelligence

Abstract

The T-spherical fuzzy set (T-SFS) has emerged as a powerful and flexible framework for modelling uncertainty and ambiguity in decision-making processes. In this study, we examine the integration of Sugeno–Weber (SW) t-norms within an interval-valued T-spherical fuzzy (IVT-SF) environment. Based on this framework, a novel family of aggregation operators is developed, including the interval-valued T-spherical fuzzy Sugeno–Weber power averaging (IVT-SFSWPA), power geometric (IVT-SFSWPG), power-weighted averaging (IVT-SFSWPWA), and power-weighted geometric (IVT-SFSWPWG) operators. The proposed operators are analyzed in terms of their fundamental properties and special cases, demonstrating their flexibility and effectiveness in handling complex decision-making problems. In addition, a new multi-attribute decision-making (MADM) method is developed within the IVT-SF environment. Furthermore, a comparative analysis with existing approaches is conducted to highlight the superiority, robustness, and practicality of the proposed aggregation operators. The results indicate that the proposed method provides more consistent and reliable decision-making outcomes. This study advances fuzzy decision-making methodologies and offers a promising approach to addressing real-world problems in dynamic, complex environments.

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