Variable weights theory and its application to multi-attribute group decision making with intuitionistic fuzzy...

Area of Science:

• Operations Research
• Decision Science
• Artificial Intelligence

Background:

• Determining decision maker (DM) weights is crucial in multi-attribute group decision making.
• Existing methods often assume constant DM weights, potentially leading to irrational outcomes.
• Intuitionistic fuzzy sets (IFSs) offer a robust framework for handling uncertainty in evaluations.

Purpose of the Study:

• To propose a novel method for determining DM weights based on variable weights theory.
• To address the limitations of constant DM weight assumptions in existing decision-making models.
• To enhance the rationality and effectiveness of group decision-making processes.

Main Methods:

• Utilizing intuitionistic fuzzy sets (IFSs) to represent DM assessments.
• Applying the intuitionistic fuzzy weighted averaging (IFWA) operator for initial weighting.
• Implementing variable weights theory to dynamically adjust DM weights.
• Calculating converted values of IFSs for final alternative selection.

Main Results:

• The proposed method successfully determines DM weights using variable weights theory.
• Demonstrated effectiveness through two illustrative examples.
• Comparative analysis shows advantages over existing approaches.
• The method leads to the acquisition of the best appropriate alternative.

Conclusions:

• The novel method effectively determines dynamic DM weights, improving decision rationality.
• Variable weights theory combined with IFSs provides a powerful tool for group decision making.
• The approach offers a more realistic and effective way to handle complex decision problems.