Approaches to dynamic multiple attribute decision making with 2-tuple linguistic information

Abstract

In this paper, we investigate the dynamic linguistic multiple attribute decision making problems with 2-tuple linguistic information. In order to aggregate dynamic 2-tuple linguistic information, some new dynamic aggregation operators are proposed: the dynamic 2-tuple weighted averaging (DTWA) operator, dynamic 2-tuple weighted geometric (DTWG) operator and dynamic 2-tuple weighted harmonic averaging (DTWHA) operator. Furthermore, some procedures based on the DTWA/DTWG/ DTWHA operators and TWA/TWG/ TWHA operators are developed to solve the dynamic 2-tuple linguistic multiple attribute decision making problems where all the decision information about attribute values takes the form of 2-tuple linguistic information collected at different periods. Finally, an illustrative example for evaluating enterprise financial performance is given to verify the developed approach and to demonstrate its practicality and effectiveness.