A Method for Uncertain Linguistic Multiple Attribute Decision Making with Incomplete Weight Information

Abstract

The aim of this paper is to investigate the multiple attribute decision making problems with uncertain linguistic information, in which the information about attribute weights is incompletely known, and the attribute values take the form of uncertain linguistic variables. We establish an optimization model based on the maximizing deviation method, by which the attribute weights can be determined. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. We utilize the uncertain linguistic weighting average (ULWA) operator to aggregate the uncertain linguistic variables corresponding to each alternative, and then rank the alternatives by means of the aggregated linguistic information. Finally, an example is shown to highlight the procedure of the proposed algorithm at the end of this paper.