On Methods For Multiple Attribute Decision Making With Linguistic Assessment Information

The multiple attribute decision-makings (MADM) is an important part of the modern decision sciences, its methods and theory have been successfully applied in a variety of fields including engineering, economics, management, teaching, etc. The nature of MADM is that these alternatives should be compared, ranked and chosen by the decision maker using the obtained decision information through a certain way. Because of the complexity of things and the fuzziness of human thoughts, decision makers often give the decision information in linguistic form. Therefore, it is worth studying the MADM problems with linguistic preference information.A few MADM methods and linguistic information aggregation operators are studied in this paper, and the major work leis in the following four parties.1. A weighted TOPSIS method is presented on the basis of the weighted TOPSIS valuation function that is newly defined, and the method is extended under fuzzy environment in which the fixation attribute fuzzy linguistic values and the normalizing method for the fuzzy number are given. Then an approach to the MADM with the fuzzy linguistic information is presented.2. Based on the objective-grade-membership matrix and the vague theory, the concepts of the favor (against, neutral) grade of each alternative to the attributes are defined, and the vague evaluation of the satisfaction of the decision maker to each alternative and the ideal vague values are defined. Then all alternatives are ranked and the best one is selected using the TOPSIS method or using a new score function.3. The operational principles of two-tuples and the conception of two-tuples vector projection are given. A projection method based on two-tuples information processing for multi-attribute group decision making with linguistic assessment is presented. It avoided both information losing which occur in the linguistic information processing and linguistic vector comparing directly.4. Based on the two-tuples weighted averaging (T-WA) operator and the two-tuples order weighted averaging (T-OWA) operator, a new two-tuples hybrid weighted averaging (T-HWA) operator which consideres the importance of both the attribute values itself and its position is defined and its properties are analyzed. A method for group decision making with linguistic information based on T-HWA operator is given.