A thermodynamic method of intuitionistic fuzzy MCDM to assist the hierarchical medical system in China

Abstract

Thermodynamics has a knowledge structure to effectively depict the stability (or instability) of a system. Considering the increasing complexity of the application problems and the fuzzy nature of human thought, intuitionistic fuzzy sets were proposed by Atanassov [Intuitionistic fuzzy set, Fuzzy Sets and Systems, vol. 20, pp: 87–96, 1986] and have become a powerful tool to portray the uncertainty of things. In order to better solve the multi-criteria decision making (MCDM) problems in an intuitionistic fuzzy environment, the paper first proposes a thermodynamic method for MCDM with intuitionistic fuzzy numbers (IFNs), which can consider both the quantity and the quality of decision information in decision making process. Then, we conduct simulations to compare the decision making results derived by the proposed method and other two commonly used decision making methods (i.e., the weighted averaging operator and the TOPSIS) in the intuitionistic fuzzy environment. Furthermore, the thermodynamic method for MCDM with IFNs is applied to assist the hierarchical medical system in West China Hospital as a case study. We can get some empirical results from the simulations and applications: (1) if the optimal selections obtained by the proposed method and one of the two commonly used methods are the same, then it is likely that the three decision making methods would obtain the same results; (2) if the optimal selections by the three methods are different, then it is often the cases that the selection by the proposed method is different from those by the two other methods. These empirical studies demonstrate that the quality of decision information has significant impacts on decision making results, which indicates that the proposed method can effectively solve the practical decision making problems.