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Partitioned Maclaurin symmetric mean operators in bipolar complex fuzzy sets for multiattribute decision making
Ubaid Ur Rehman1, Ibrahim Aldayel2, Meraj Ali Khan2
1Department of Mathematics, University of Management and Technology, C-II, Johar Town, Lahore, 54700, Punjab, Pakistan.
Scientific Reports
|April 24, 2025
Summary
This study introduces new mathematical tools, bipolar complex fuzzy partitioned Maclaurin symmetric mean operators, to handle complex uncertainties. These operators enhance decision-making processes in ambiguous situations.
Area of Science:
- Mathematics
- Decision Sciences
Background:
- Mathematical tools are essential for managing uncertainty and ambiguity.
- Bipolar complex fuzzy sets offer a method for handling dual-aspect and second-dimensional information simultaneously.
Purpose of the Study:
- To introduce novel aggregation operators within the bipolar complex fuzzy set framework.
- To develop a multi-attribute decision-making technique using these operators to address complex uncertainties.
Main Methods:
- Propounding bipolar complex fuzzy partitioned Maclaurin symmetric mean and bipolar complex fuzzy partitioned dual Maclaurin symmetric mean operators.
- Introducing weighted versions of these operators and their associated axioms.
- Developing a multi-attribute decision-making methodology based on the proposed operators.
Main Results:
- The study successfully defines and formulates new aggregation operators for bipolar complex fuzzy sets.
- A practical decision-making technique is established, demonstrating the operators' utility.
- The proposed operators are validated through an example and comparison with existing methods.
Conclusions:
- The newly developed aggregation operators effectively manage complex uncertainties in decision-making.
- The proposed technique offers a reliable and practical approach for multi-attribute decisions within bipolar complex fuzzy environments.
- The research contributes novel mathematical tools for handling ambiguity in complex data.
Keywords:
Bipolar complex fuzzy setsDecision making techniquePartition Maclaurin/partition dual Maclaurin mean operatorsMore Related Videos
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