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T-Spherical 2-Tuple Linguistic Aggregation Operators and Their Applications in the Decision-Making Strategy

Author

Listed:
  • Tahir Mahmood
  • Zeeshan Ali
  • Amrullah Awsar
  • Ardashir Mohammadzadeh

Abstract

The decision-making technique is very important and valuable and is used for evaluating the best decision from the collection of preferences. In this manuscript, we elaborated the novel concept of the T-spherical 2-tuple linguistic (TS2-TL) set and its operational laws in the consideration of t-norm and t-conorm. The theory of the TS2-TL set is more massively generalized than the fuzzy set and its extensions. Moreover, aggregating the collection of information into a singleton set is also a very challenging task for scholars; therefore, by using the TS2-TL setting, we explored the TS2-TL weighted averaging (TS2-TLWA), TS2-TL ordered weighted averaging (TS2-TLOWA), TS2-TL hybrid averaging (TS2-TLHA), TS2-TL weighted geometric (TS2-TLWG), TS2-TL ordered weighted geometric (TS2-TLOWG), and TS2-TL hybrid geometric (TS2-TLHG) operators and described their important and dominant properties. The derived operators based on TS2-TL information are the modified version of the averaging/geometric aggregation operators for fuzzy set and their extensions. Using the invented approaches, various specific terminologies are also diagnosed with the help of remarks. The elaborated operators are applied to determine and rank the manufacturing of small and medium-sized enterprises (SMEs). Finally, we utilize the sensitive analysis of the exposed approaches with various existing drawbacks in the presence of many practical examples to expose the improvements, dominance, and graphical representations of utilized approaches.

Suggested Citation

  • Tahir Mahmood & Zeeshan Ali & Amrullah Awsar & Ardashir Mohammadzadeh, 2023. "T-Spherical 2-Tuple Linguistic Aggregation Operators and Their Applications in the Decision-Making Strategy," Mathematical Problems in Engineering, Hindawi, vol. 2023, pages 1-24, May.
  • Handle: RePEc:hin:jnlmpe:6098162
    DOI: 10.1155/2023/6098162
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