A Correlation Based Circular Intuitionistic Fuzzy TOPSIS for Hospital Location Problem

The healthcare system is the most affected sector during COVID-19. In this pandemic, the sustenance of the system becomes challenging due to insufficient equipment, low hospital-patient capacity, lack of specialized human resources, and high infection rates in hospitals. For a patient, the selection of an appropriate hospital under many combating criteria is quite difficult. The hospital selection is posed as multicriteria decision-making (MCDM) problem, called the hospital location problem. While solving this problem, the exact allocation of membership and non-membership values 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 the uncertainty and hesitancy involved in hospital selection. This paper proposes a circular intuitionistic fuzzy TOPSIS using a correlation coefficient for the hospital location selection problem. The proposed method is validated over a hospital location problem in Istanbul, Turkey, given by Alkan, Nurşah, and Cengiz Kahraman [Journal of Intelligent & Fuzzy systems 8(2021): 1-22].