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Optimizing decision-making with aggregation operators for generalized intuitionistic fuzzy sets and their
Muhammad Wasim1, Awais Yousaf2, Hanan Alolaiyan3
1Department of Mathematics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
This study introduces generalized intuitionistic fuzzy sets (GIFSBs) and new aggregation operators to improve decision-making. These novel operators enhance the evaluation of complex scenarios, such as startup success in the tech industry.
Area of Science:
- Fuzzy Set Theory
- Decision Sciences
- Artificial Intelligence
Background:
- Intuitionistic fuzzy sets (IFSs) extend classical fuzzy sets (FSs).
- There is a need for advanced frameworks to handle uncertainty in decision-making.
- Generalized intuitionistic fuzzy sets (GIFSBs) offer a more flexible approach.
Purpose of the Study:
- To advance intuitionistic fuzzy set theory to generalized intuitionistic fuzzy sets (GIFSBs).
- To introduce novel aggregation operators (GIFWAA, GIFWGA, GIFOWAA, GIFOWGA) for GIFSBs.
- To enhance decision-making capabilities by aligning aggregated values with preferences for optimal outcomes.
Main Methods:
- Development of new aggregation operators for GIFSBs with properties like idempotency, boundedness, monotonicity, and commutativity.
- Analysis of the relationships and applicability of these novel operators.
- Application of the operators in a multiple-criteria decision-making (MCDM) process.
Main Results:
- Introduction of four new aggregation operators specifically designed for GIFSBs.
- Demonstration that the aggregated values align with generalized intuitionistic fuzzy numbers (GIFNs).
- Successful application of the proposed operators in a practical MCDM problem.
Conclusions:
- The developed operators provide a robust framework for decision-making under uncertainty.
- GIFSBs and their associated operators offer significant advantages over existing fuzzy set theories.
- The study provides a practical tool for evaluating complex scenarios like tech startup success.
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