Abstract
In this paper, we investigate the linguistic multiple attribute group decision making (MADM) problems in which the attributes and experts are in different priority level. Motivated by the ideal of prioritized aggregation operators (R.R. Yager, Prioritized aggregation operators, International Journal of Approximate Reasoning 48(2008) 263-274.), we develop some prioritized aggregation operators for aggregating linguistic information, and then apply them to develop some models for linguistic multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Finally, a practical example about talent introduction is given to verify the developed approach and to demonstrate its practicality and effectiveness.