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.