The Study Of Approaches To Multicriteria Decision Making Based On Incomplete Information

Generally, multicriteria decision making(MCDM) is classified into multiattribute decision making(MADM) and multiobjective decision making(MODM), and it forms an important part of the decision analysis theory. MCDM is defined by making preference decisions(e.g. evaluation, prioritization and selection) over the available alternatives that characterized by multiple, usually conflicting criteria. However, in many real world situations, the decision maker is willing or able to provide only incomplete information on parameters because of time pressure and lack of knowledge or data. Therefore, making study of MCDM with incomplete information is significant in theory and practice.In this paper, several special types of incomplete information are dealt with. The major work is in the following.1. Two models are constructed for MADM with incomplete information about three parameters, attribute weights, attribute values and utility values of alternatives.2. A new special type of incomplete information of MADM is presented and a corresponding model is constructed.3. A stochastic simulation method is given for attribute weights of MADM being incomplete. Then, the method is extended for attribute weights and attribute values of MADM being incomplete. Finally, the method is applied to the domain criteria of MADM.4. A new solution for interval number linear programming is presented and a chance-constrained approach to multiobjective linear programming with interval coefficients is proposed.5. The corresponding algorithms about above models or approaches are developed and the corresponding numerical examples are demonstrated to show the effectiveness and feasibility.