Large-scale group decision consensus under social network: A chance-constrained robust optimization-based minimum cost consensus model

For a proper management of large-scale group decision-making (LSGDM) problems, it is essential to consider multiple factors such as the relationship between the decision-makers, the dimensionality reduction, and the cost of guaranteeing that the involved decision-makers reach an agreement. Therefore, this paper proposes an adaptive consensus framework for LSGDM to efficiently derive agreed decisions when many decision-makers are required to participate in the decision process. First, the notion of trust propagation is applied to construct a trust network between decision-makers, which is combined with the similarity between their opinions to classify them into clusters that are weighted according to their size, and the concept of harmony that is measured by the harmony degree. Afterwards, a consensus-reaching process is carried out taking into consideration both intra- and inter-cluster consensus degrees. According to the intra- and inter-consensus level of each cluster, we propose an adaptive process with three different adjustment mechanisms based on chance-constrained robust minimum cost consensus models. The performance of the proposed framework is then illustrated by a numerical experiment. Finally, a sensitivity analysis is performed to show the validity of the proposed model.