NOVEL APPROACHES TO MULTI-CRITERIA DECISION MAKING WITH INCOMPLETE INFORMATION SYSTEM

Our main work in this study is to make a detailed discussion on the multi-criteria decision making with incomplete information systems. At first, an algorithm is constructed to retrieve the missing criteria values by taking into account the local similarity as well as global similarity of each two alternatives. Then, in view of different evaluation information representation, we establish different making methods for the corresponding completed information system. By transforming interval-valued information into intuitionistic fuzzy number, the cosine similarity measure based method is introduced to the decision making problem with interval-valued evaluation information. Moreover, the aggregation operator based method is established for set-valued information. Especially, we propose a novel decision making approach for the hybrid evaluation information from viewpoint of rough set theory. The validity of these decision making methods are demonstrated by corresponding synthetic examples.