Decision Making Method for Dual Uncertain Information based on Grey Incidence Analysis and Grey Relative Entropy Optimization

In this paper, we investigate multi-attribute group decision-making (MAGDM) problems with dual uncertain information. On the basis of considering the views of the majority, we first set the initial weights of decision makers (DMs) by the synthetic degrees of grey incidence. Then we estimate the weight ranges of attributes by the dissimilarity degrees between attributes. Furthermore based on the view of dual information optimally fitting to each other, the relative entropy minimization model is proposed to revise the weights of DMs and attributes. Finally alternatives are ranked by the absolute degrees of grey incidence to the ideal alternative. The, method proposed in this paper is illustrated with a practical case.