An Improvement of Consensus in Group Decision-Making Through an Optimal Distribution of Information Granularity

Information granularity is an essential concept of Granular Computing that has successfully been applied in group decision-making to improve the consensus. Unlike the existing approaches, in which a uniform distribution of information granularity has been employed, this study proposes to improve the consensus by invoking a process of an optimal information granularity distribution across the experts' assessments provided in the form of fuzzy preference relations. An illustrative example and some simulated group decision-making problems are conducted to show evidence of its effectiveness. The results demonstrate that this optimal process, by being more flexible than the ones assuming that the information granularity is distributed in a uniform way, reaches a higher level of consensus.