Dynamic linguistic weighted averaging operators applied to decision making

The dynamic multiple attribute decision making problems with linguistic information are investigated. A new aggregation operator called dynamic linguistic weighted averaging (DLWA) operator is presented. Based on the DLWA and LWAA operators, we develop a procedure to solve the we develop an approach to the dynamic linguistic multiple attribute decision making (DL-MADM) problems where all decision information about values takes the form of linguistic variables collected at different periods. Finally, an illustrative numerical example is also given.