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Some series of intuitionistic fuzzy interactive averaging aggregation operators
1School of Mathematics, Thapar University Patiala, Patiala, Punjab 147004 India.
Springerplus
|July 22, 2016
Summary
This study introduces new intuitionistic fuzzy averaging aggregation operators to address limitations in existing methods. These novel operators improve decision-making by considering hesitation degrees within intuitionistic fuzzy sets.
Area of Science:
- Fuzzy Mathematics
- Decision Sciences
- Artificial Intelligence
Background:
- Existing intuitionistic fuzzy aggregation operators have limitations.
- Hesitation degree is a crucial factor in intuitionistic fuzzy sets (IFSs) that needs better consideration.
- There is a need for improved operators for multi-criteria decision-making (MCDM) under IFS environments.
Purpose of the Study:
- To propose new operational laws for IFSs that account for hesitation degrees.
- To develop novel intuitionistic fuzzy averaging aggregation operators: IFHIWA, IFHIOWA, and IFHIHWA.
- To establish a new MCDM method utilizing these advanced operators for alternative selection.
Main Methods:
- Development of a new operational law for IFSs incorporating hesitation.
- Introduction of intuitionistic fuzzy Hamacher interactive weighted averaging (IFHIWA), ordered weighted averaging (IFHIOWA), and hybrid weighted averaging (IFHIHWA) operators.
- Analysis of properties like idempotency, boundedness, and homogeneity.
- Application of proposed operators in a multi-criteria decision-making framework.
Main Results:
- Successfully proposed and defined new intuitionistic fuzzy averaging aggregation operators.
- Demonstrated the effectiveness of the new operators in handling hesitation degrees.
- Validated the proposed MCDM method through comparative analysis with existing approaches.
Conclusions:
- The new operational laws and aggregation operators offer a significant improvement for IFSs.
- The developed MCDM method provides a robust tool for complex decision-making scenarios.
- The proposed operators show superior performance compared to existing methods in handling uncertainty.
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