Intuitive fuzzy multi-expert & multi-criteria decision making methodology: An application in healthcare industry

Abstract

Fuzzy set extensions have been one of the uncertainty handling tools used in many studies in recent years. Since the 1990s, fuzzy decision making theory has been one of the most studied topics in the literature. Specifically in recent years, many fuzzy decision making models have been proposed by using new fuzzy set extensions. Multi-criteria decision making models have been a research area where these extensions are frequently used. In this study, an integrated AHP-TOPSIS methodology employing triangular intuitionistic fuzzy numbers has been transformed into a methodology in which interval-valued triangular fuzzy numbers can be used. As an application, a surgery robot evaluation and selection problem for a newly founded private hospital to be a part of a hospital chain in Istanbul, has been analyzed. The obtained results showed that different fuzzy set extension based datasets can be successfully used in the same methodology by converting them to each other. In this study, comparison and sensitivity analyses are also performed and it has been observed that the results of the proposed model are quite robust and consistent.