A goal programming model for solving the incomplete stochastic multi-attribute decision making

This paper proposes a novel method for the stochastic multi-attribute decision making problems, in which the attribute weights are partial known. The deviation of attribution values and decision maker's subjective information is combined to obtain the weights of attribution by a goal programming model. Furthermore, the expected utility and decision-maker's attitudes toward risk are considered to determine the ranking order of all alternatives. Finally, a numerical example is used to show the effectiveness and applicability of the developed method.