TOPSIS Method for Multiple Attribute Decision Making with Incomplete Weight Information in Linguistic Setting

Abstract The aim of this paper is to investigate the multiple attribute decision making problems with linguistic information, in which the information about attribute weights is incompletely known, and the attribute values take the form of linguistic variables. We develop a new method to solve linguistic MADM with incomplete weight. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional TOPSIS, by which the attribute weights can be determined. Based on this model, we develop a TOPSIS method to rank alternatives and to select the most desirable one(s). Finally, an example is shown to highlight the procedure of the proposed algorithm at the end of this paper.