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Innovative intuitionistic fuzzy fairly aggregation operators with linear programming based decision-making approach
Muhammad Riaz1, Hafiz Muhammad Athar Farid1, Rukhsana Kausar1
1Department of Mathematics, University of the Punjab, Lahore, Pakistan.
Journal of Ambient Intelligence and Humanized Computing
|June 8, 2023
Summary
This study introduces new operational rules and aggregation operators for intuitionistic fuzzy sets (InFS) to improve decision-making with incomplete information. The developed methods offer fair solutions for multi-criteria decision-making (MCDM) problems.
Area of Science:
- Decision Sciences
- Fuzzy Logic Systems
- Operations Research
Background:
- Intuitionistic fuzzy set (InFS) theory offers a robust framework for strategic planning and decision-making, particularly when dealing with uncertainty.
- Aggregation operators (AOs) are crucial for synthesizing information in decision processes, but challenges arise with incomplete data.
- Existing methods struggle to provide fair and neutral solutions in complex decision environments with limited information.
Purpose of the Study:
- To develop novel operational rules and aggregation operators within the intuitionistic fuzzy set environment.
- To introduce a fair approach for handling intuitionistic fuzzy information, utilizing proportional distribution.
- To construct a fair multi-criteria decision-making (MCDM) method using the proposed operators and addressing partial weight information.
Main Methods:
- Development of new operational laws for InFS based on proportional distribution.
- Construction of fair aggregation operators for intuitionistic fuzzy numbers.
- Integration of a linear programming model to determine criteria weights with partial information.
- Formulation of a fair MCDM method incorporating multiple decision-makers' evaluations.
Main Results:
- Novel operational rules and fair aggregation operators for InFS were successfully established.
- A fair MCDM method was constructed, demonstrating efficacy in handling incomplete information and partial weights.
- The proposed method provides a neutral or fair remedy for decision-making challenges in InFS environments.
- A rigorous implementation validated the effectiveness of the developed aggregation operators.
Conclusions:
- The proposed operational rules and aggregation operators enhance decision-making capabilities within the intuitionistic fuzzy set framework.
- The developed fair MCDM method effectively addresses challenges posed by incomplete information and partial weight data.
- This research contributes innovative tools for strategic planning and complex decision analysis under uncertainty.
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