A multi-attribute decision making method based on evidence theory and average operator

Aggregating decision-maker's evaluation and acquiring a ranking of alternatives is very important to multiple attribute decision making (MADM). In this paper, we propose a new method to solve MADM problems in which all the information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy numbers (IVIFNs). In the method, IVIFNs are converted into a group of basic probability assignment (BPA) by continuous interval argument ordered weighted average (C-OWA) operator. And then evidence theory is applied to aggregate BPAs into a comprehensive BPA. Based on this single overall BPA, a ranking order of candidates can be obtained. Finally, a numerical example is used to illustrate the effectiveness of the proposed method.