Two Models For Multiple Attribute Decision Making With Incomplete Information And Its Sensitivity Analysis

A group of optimization models which aggregate interval belief degree of each attribute are proposed for multiple attribute decision making problems with incomplete information and interval belief structure, the overall belief degree of each alternative which is assessed to assessment grade separately can be obtained. Using the optimization model, a constrained optimization model which computes the expected utility is developed. By solving the optimization problem, we get the expected utility to rank each alternative. Finally, sensitivities associated with attribute weight and belief degree are analyzed, and then a sensitivity analysis method for the alternative's ranking results associated with decision making parameters is conducted.In consideration of incomplete information in the process of making decision, a class of multiple attribute group decision making problem with incomplete information is studied. On the base of this research, a series of nonlinear programming model is estabilished, and then we get interval numbers of alternatives' utility value. At the same time, individual optimization makes use of possibility degree to compare each two alternatives to obtain alternative ranking. In the process of preference aggregation, the concept of "ranking index value of the alternative" is introduced, we also consider ranking index value which is given by individuals, and incomplete information of decision maker's importance. Finally, a numerical example is giver to demonstrate that the proposed approach can make reasonable decision under incomplete information.