Multi-Attribute Decision-Making Based on Bonferroni Mean Operators under Cubic Intuitionistic Fuzzy Set Environment.

Area of Science:

• Uncertainty Quantification
• Fuzzy Set Theory
• Decision Sciences

Background:

• Cubic intuitionistic fuzzy (CIF) sets offer a hybrid approach to represent complex data, integrating interval-valued and intuitionistic fuzzy set characteristics.
• Existing research lacks aggregation operators specifically designed for CIF sets, limiting their application in decision-making.
• Aggregation operators are crucial for consolidating diverse preferences in decision problems.

Purpose of the Study:

• To propose novel Bonferroni mean and weighted Bonferroni mean averaging operators for cubic intuitionistic fuzzy numbers.
• To develop a new decision-making methodology within the CIF environment utilizing these operators.
• To address the gap in aggregation operator research for CIF sets.

Main Methods:

• Introduction of new Bonferroni mean and weighted Bonferroni mean averaging operators tailored for cubic intuitionistic fuzzy numbers.
• Development of a decision-making algorithm based on the proposed CIF aggregation operators.
• Application and validation of the proposed method using a numerical example and comparative analysis.

Main Results:

• Successfully proposed and defined new Bonferroni mean and weighted Bonferroni mean averaging operators for CIF numbers.
• Developed a practical decision-making method that effectively aggregates preferences in a CIF environment.
• Demonstrated the applicability and feasibility of the new method through a comparative analysis.

Conclusions:

• The proposed aggregation operators and decision-making method offer a significant advancement for handling uncertainties with CIF sets.
• The developed approach provides a robust framework for decision-making problems involving complex, uncertain data.
• The study highlights the potential of CIF sets and novel aggregation techniques in various quantitative fields.