Abstract
We investigate the multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of 2-tuple linguistic information. Motivated by the ideal of dependent aggregation [Xu, Z. S. (2006). Dependent OWA operators. Lecture Notes in Artificial Intelligence, 3885, 172–178], in this paper, we develop some dependent 2-tuple linguistic aggregation operators: the dependent 2-tuple ordered weighted averaging (D2TOWA) operator and the dependent 2-tuple ordered weighted geometric (D2TOWG) operator, in which the associated weights only depend on the aggregated 2-tuple linguistic arguments and can relieve the influence of unfair 2-tuple linguistic arguments on the aggregated results by assigning low weights to those “false” and “biased” ones and then apply them to develop some approaches for multiple attribute group decision making with 2-tuples linguistic information. Finally, some illustrative examples are given to verify the developed approach and to demonstrate its practicality and effectiveness.
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