T-Spherical fuzzy soft rough aggregation operators and their applications in multi-criteria group decision-making

• Published in: Granular computing (Internet) 2024-03, Vol.9 (1), Article 6
• Main Authors: Farman, Shabana, Khan, Faiz Muhammad, Bibi, Naila
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computational Intelligence; Engineering; Original Paper
• ISSN: 2364-4966; 2364-4974
• DOI: 10.1007/s41066-023-00437-3

Where a person’s choice is not limited to just” no” or” yes,” but encompasses some other form of characteristics of rejection, T-Spherical fuzzy soft sets are significantly important in like situations. T-Spherical fuzzy soft sets and fuzzy rough sets are two common techniques for working with information that lacks precision and is vague in nature. The most pervasive characteristic of naturally occurring events is uncertainty. As a result, the development of techniques to address such situations become enviable. Also, consider that aggregation operators are highly efficient tools for reducing the volume of data to a single value, which greatly assists in resolving decision-making issues. Therefore, we propose a framework for addressing multi-criteria group decision-making (MCGDM) problems using a novel approach, based on T-Spherical fuzzy soft rough sets. In this manuscript, we propose T-Spherical fuzzy soft rough sets (TSFSRSs) for a more effective modeling of uncertainties and ambiguity in data. This intriguing model has been expertly constructed to take advantage of the most general mathematical frameworks for describing parameterized conflicting information. We have demonstrated the significant adaptability of the proposed strategy by implementing its methodology in real-world practical applications. The proposed technique has skillfully surpassed the current approaches in terms of representatively capabilities. The operators in this manuscript define a mathematical function in the area of information theory that combines all the received data as input and produces a single output component. Some averaging aggregation operators, such as hybrid averaging operators, ordered-weighted averaging operators, weighted averaging operators, and geometric aggregation operators, such as hybrid geometric operators, ordered-weighted geometric operators, and weighted geometric operators, are described. Additionally, the characteristics of T-Spherical Fuzzy Soft Rough values are discussed. These are relevant to various Multi-criteria Group Decision-Making (MCGDM) problems. There is also a description of an algorithm for a decision-making problem that utilizes the proposed aggregation operators of different types. We define score function and accuracy function. A numerical solution has also been provided to demonstrate the proposed methodology for solving real-life problems. Finally, the explored method is compared with the existing methods to demonstrate that the exp