A novel distance measure for pythagorean fuzzy sets and its applications to the technique for order preference by similarity to ideal solutions

Ever since the introduction of Pythagorean fuzzy (PF) sets, many scholars have focused on solving multicriteria decision-making (MCDM) problems with PF information. The technique for order preference by similarity to ideal solutions (TOPSIS) is a wellknown and effective method for MCDM problems. The objective of this study is to extend the TOPSIS to tackle MCDM problems under the PF environment. In this study, we develop a novel distance measure that considers the length, the angle, and the greater space, which reflect the properties of PF sets. Then, we apply the proposed distance measure in PF-TOPSIS to calculate the distances from the PF positive ideal solution and the PF negative ideal solution. Finally, we take the evaluation of emerging technology commercialization as an MCDM problem to illustrate the proposed approaches, and we then compare these approaches to demonstrate the scalar type PF-TOPSIS is the most feasible and effective approach in practice.