Improved MABAC method based on single-valued neutrosophic 2-tuple linguistic sets and Frank aggregation operators for MAGDM

In this paper, multiple attribute group decision making(MAGDM) problems with single-valued neutrosophic 2-tuple linguistic (SVN2TL) set information are presented based on Frank operator, extend multi-attributive border approximation area comparison (MABAC) method, and best worst method (BWM). We first give the the concept of BWM method, Frank operator, and basic operational rules on SVN2TL set with Frank t-norms and t-conorms. Then, two aggregation operators including SVN2TL Frank weighted averaging (SVN2TLFWA) operator and Frank weighted geometric (SVN2TLFWG) operator are developed, and some desirable properties are discussed as well. What’s more, an iterative algorithm is designed for the determination of decision makers’ weight based on BWM method. Subsequently, combine the extend MABAC method and proposed operators, a new approach is developed to deal MAGDM with SVN2TL information. Finally, a numerical example has been given to show the procedure of the proposed method, and some sensitivity and comparative analysis are also conducted to illustrate the effectiveness and superiority of the proposed method.