Large group decision-making with a rough integrated asymmetric cloud model under multi-granularity linguistic environment

Large group decision-making often contains strong uncertainty and randomness due to the complexity of decision-making problems. Moreover, existing methods about large group decision-making usually assume that the relationships among decision-makers are mutually independent, which will neglect the relevance of decision information to some extent. To address these limitations, this paper proposes a novel rough integrated asymmetric cloud model to tackle large group decision-making under multi-granularity linguistic environment. This model can not only flexibly portray decision-makers' preferences, but also objectively handle the uncertainty and randomness in large group decision-making through the relevance of preferences. Firstly, a trust propagation model based on rough integrated asymmetric cloud is raised to consider the impact of uncertainty and randomness in the trust propagation on large group decision-making. Secondly, a new asymmetric cloud distance and similarity are proposed to overcome some defects of existing methods. Thirdly, a decision-maker weight calculation method based on the Shapley function is advanced to enhance the decision-making reasonableness. Next, an investment problem is solved by the proposed method. Finally, the effectiveness and superiority of the proposed method are demonstrated by the sensitivity and comparison analyses.