Consensus of large-scale group decision making in social network: the minimum cost model based on robust optimization (Q2056346)

This paper studies the large scale group decision making consensus over social networks concerning the minimum cost. The paper proposes an expert clustering in social networks to enhance the consensus reaching using robust optimization technique. A consensus index is used to access the opinion difference between individuals in the network. The unit adjustment cost is considered to be limited by an ellipsoidal set. The model optimizes the total compensation cost in different scenarios on the basis of robust optimization. It is shown that the consensus reaching process of offline large scale group decision making is reached. Some comparison numerical examples have been performed to illustrate the algorithm.