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Intuitionistic fuzzy interaction bonferroni means and its application to multiple attribute decision making
IEEE Transactions on Cybernetics
|June 27, 2014
Summary
This study introduces new intuitionistic fuzzy Bonferroni mean operators that account for interactions between membership and non-membership functions. These novel methods enhance decision-making in complex scenarios involving intuitionistic fuzzy sets.
Area of Science:
- Decision Sciences
- Fuzzy Mathematics
- Operations Research
Background:
- The Bonferroni mean (BM) is a widely used aggregation operator for capturing interrelationships between arguments.
- Existing intuitionistic fuzzy BMs (IF-BMs) inadequately address the interplay between membership and non-membership functions of intuitionistic fuzzy sets (IFSs).
Purpose of the Study:
- To develop novel intuitionistic fuzzy Bonferroni mean operators that incorporate interactions between membership and non-membership functions of IFSs.
- To extend the applicability of Bonferroni means in intuitionistic fuzzy environments by considering these interactions.
Main Methods:
- Development of the intuitionistic fuzzy interaction Bonferroni mean (IFIBM) and weighted intuitionistic fuzzy interaction Bonferroni mean (WIFIBM) operators.
- Investigation of the properties and special cases of the newly developed operators.
- Application of the proposed operators to multiple attribute decision-making (MADM) problems.
Main Results:
- The proposed IFIBM and WIFIBM operators effectively capture the interactions between membership and non-membership functions in IFSs.
- The study details the procedural steps for applying these operators in MADM.
- A numerical example demonstrates the validity and feasibility of the proposed approach.
Conclusions:
- The new intuitionistic fuzzy interaction BM operators offer a more comprehensive way to handle decision-making under uncertainty with IFSs.
- These operators provide a valuable extension to existing BM generalizations in intuitionistic fuzzy environments.
- The demonstrated MADM approach using these operators is effective and practical.
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