Does Intuitionistic Fuzzy Analytic Hierarchy Process Work Better Than Analytic Hierarchy Process?

Analytical hierarchy process (AHP) has been prevailing in multicriteria decision-making (MCDM) problems. Meanwhile, the fuzzy sets family has shown great power in representing vagueness and advancing the decision quality under uncertainties. The literature well documented the foundation and advantages of their integration, such as Fuzzy AHP. However, under what conditions do such integrations surely perform better than AHP in the MCDM process is still unclear. In this paper, we pick intuitionistic fuzzy (IF) sets-one of the advanced and most prevailing members of the fuzzy sets family, and further investigate when and how to integrate IF and AHP (IF-AHP) to the best advantage. We illustrate the formulated quantitative differences between the weight of AHP and the normalized defuzzified weight of IF-AHP. We uncover the qualitative and quantitative differences between AHP and IF-AHP, and identify the conditions and strategies of using IF-AHP instead of AHP. We use data experiments to illustrate our findings and further implement two case studies based on the real scenarios of supplier selection for validation and explanation.

Automated summary

This paper investigates the integration of intuitionistic fuzzy (IF) sets and Analytical Hierarchy Process (AHP) to maximize advantages. Quantitative differences between AHP weights and normalized defuzzified IF-AHP weights are illustrated, revealing qualitative and quantitative disparities between AHP and IF-AHP. The study identifies conditions and strategies for utilizing IF-AHP over AHP, with data experiments and case studies for validation.