Generalized Orbicular (m,n,o) T-Spherical Fuzzy Sets with Hamacher Aggregation Operators and Application to Multi-Criteria Group Decision Making

TL;DR

This paper introduces a new generalized fuzzy set model with adjustable parameters for better uncertainty representation, along with aggregation operators, and applies it to multi-criteria group decision making for selecting online shopping platforms.

Contribution

The paper proposes the Generalized Orbicular (m,n,o) T-Spherical Fuzzy Set (GO-TSFS), extending existing fuzzy models with adjustable parameters and new aggregation operators for improved decision-making.

Findings

Enhanced accuracy in representing vague and imprecise data.

Effective application of the model to select optimal e-commerce platforms.

Sensitivity analysis confirms robustness of the decision-making approach.

Abstract

This paper introduces a novel approach to enhance uncertainty representation, offering decision-makers a more comprehensive perspective for improved decision-making outcomes. We propose Generalized Orbicular (m,n,o) T-Spherical Fuzzy Set (GO-TSFS), a flexible extension of existing fuzzy set models including Globular T-spherical fuzzy sets (G-TSFSs), T-spherical fuzzy sets (T-SFSs), (p,q,r) Spherical fuzzy sets, and (p,q) Quasirung orthopair fuzzy sets (QOFSs). The framework employs three adjustable parameters m, n, and o to finely tune the influence of membership degrees, allowing for adaptable weighting of various degrees of membership. By utilizing spheres to represent membership, indeterminacy, and non-membership levels, the model enhances accuracy in depicting vague, ambiguous, and imprecise data. Building upon the foundation of GO-TSFSs, we introduce essential set operations and algebraic operations for GO-TSF Values (GO-TSFVs). Moreover, we also develop score functions, accuracy functions, and basic distance measures such as Hamming and Euclidean distances to further enhance the analytical capabilities of the framework. Additionally, we propose GO-TSF Hamacher Weighted Averaging (GO-TSFHWA) and GO-TSFH Weighted Geometric (GO-TSFHWG), aggregation operators tailored for our proposed sets. To demonstrate the practical applicability of our approach, we apply our proposed aggregation operators namely GO-TSFHWA and GO-TSFHWG to solve a Multi-Criteria Group Decision Making (MCGDM) problem, specifically for selecting the most suitable e-commerce online shopping platform from the top-rated options. Sensitivity analysis is also conducted to validate the reliability and efficacy of our results, affirming the utility and robustness of the proposed methodology in real-world decision-making scenarios.