Linguistic intuitionistic fuzzy hamy mean operators and their application to multiple-attribute group decision making

Abstract

The Hamy Mean (HM) operator is a useful aggregation tool characterized by considering the correlation between multiple integral parameters. The linguistic intuitionistic fuzzy numbers (LIFNs) use linguistic variables (LVs) to represent membership degree (MD) and non-membership degree (NMD), which can flexibly and accurately represent the ambiguity in the actual decision problem. Based on the operational laws of LIFNs, we extend the HM operator to the LIFNs, and put forward the HM operator for LIFNs (LIFHM) and the weighted HM operator for LIFNs (WLIFHM). In addition, we discuss the properties of LIFHM and WLIFHM operators and some special cases, then we propose a multiple-attribute group decision making (MAGDM) method based on the WLIFHM operator. Lastly, a practical example is given to illustrate the effectiveness of the proposed operators by comparing it with different methods.