Circular Intuitionistic Fuzzy GRA for Hospital Location Problem

The healthcare system is the most affected sector during COVID-19, as its performance has deteriorated badly. The sustenance of the system in this pandemic is challenging due to insufficient equipment, low hospital-patient capacity, lack of specialized human resources, and high infection rates in the hospitals. The selection of an appropriate hospital for a patient under many combating criteria is a hospital location problem that falls under the umbrella of multicriteria decision-making (MCDM). The exact allocation of membership and non-membership value to a hospital is tedious, so circles are used to imitate the membership and non-membership values instead of points. The circular intuitionistic fuzzy set (CIFS) is a recent extension of Atanassov's intuitionistic fuzzy set (AIFS), in which circular membership and circular non-membership models have uncertainty and hesitancy. This paper proposes a circular intuitionistic fuzzy GRA for the hospital location selection problem. The proposed method is validated over a hospital location problem in Istanbul, Turkey, given by Alkan, Nursah, and Cengiz Kahraman [Journal of Intelligent & Fuzzy systems 8(2021): 1–22].