An Integrated Pythagorean Fuzzy TOPSIS Framework for Supplier Selection with Uncertain Attribute Weights

Abstract

The significance of attribute weights is paramount in supplier selection, a classic multi-criteria decision-making (MCDM) problem. Conventional MCDM models typically treat these weights as predetermined, which does not reflect real-world ambiguity. To bridge this gap, our work incorporates scenarios where weights are known, entirely unknown, or partially specified as intervals. We introduce a novel framework operating within a Pythagorean fuzzy environment, which provides a more powerful and less restrictive way to capture vagueness. Within this framework, we apply the TOPSIS technique adapted for Pythagorean fuzzy sets, utilizing spherical distance to calculate each supplier's proximity to the ideal solution. The proposed model's applicability and strength are demonstrated through a numerical example involving five potential suppliers.