An intuitionistic fuzzy entropy approach for supplier selection

Area of Science:

• Decision Sciences
• Fuzzy Logic
• Operations Research

Background:

• Intuitionistic Fuzzy Sets (IFS) offer flexibility in handling imprecise data and uncertainty.
• IFS concepts enable more human-consistent reasoning with imperfect information.
• Existing decision-making methods may not fully capture complex uncertainties.

Purpose of the Study:

• To develop a novel fuzzy decision-making approach using intuitionistic fuzzy entropy.
• To apply this approach for supplier selection and ranking based on multiple attributes.
• To determine criterion weights objectively using the proposed entropy measure.

Main Methods:

• Application of entropy notions within the framework of intuitionistic fuzzy sets.
• Formulation of an entropy-based model for decision-making.
• Calculation of alternative weights using intuitionistic fuzzy entropy measure.
• Validation through a real-world case study on supplier rankings.

Main Results:

• The proposed intuitionistic fuzzy entropy measure effectively ranks suppliers.
• Objective weights for criteria are derived, reducing reliance on subjective expertise.
• The method provides a systematic way to handle uncertainty in supplier selection.

Conclusions:

• The intuitionistic fuzzy entropy measure is a valuable tool for complex supplier selection problems.
• This approach enhances decision-making transparency and reliability.
• It offers a robust alternative for evaluating and ranking suppliers under uncertainty.