A novel WASPAS approach for multi-criteria physician selection problem with intuitionistic fuzzy type-2 sets

Abstract

Due to innovative and practical technology, the selection of a right physician is an important issue for patients. However, uncertainty and vagueness frequently arise during the process of selecting physicians. Intuitionistic fuzzy type-2 sets (IFT2Ss) (recently named as Pythagorean fuzzy sets) provide an important tool to handle the uncertainty arises in real-life decision-making problems. This paper presents an extended weighted aggregated sum product assessment (WASPAS) method based on novel information measures (entropy and divergence measures) and operators under IFT2Ss context. In the proposed WASPAS method, entropy and divergence measure-based formula is developed to find the criteria weights. For this, several intuitionistic fuzzy entropy and divergence measures are developed for IFT2Ss. To increase the stability of the proposed methodology, the criteria’s weights are calculated in the form of objective and subjective weights. Further, to reveal the applicability and effectiveness of proposed method, an uncertain multi-criteria decision-making problem of physician selection is executed with intuitionistic fuzzy information of second type. Finally, the validity of the proposed method is implemented by comparison with existing methods and sensitivity analysis and also proves that the proposed method is valid and feasible in the physician selection processes with information given in intuitionistic fuzzy type-2 numbers (IFT2Ns).