Conflict management-based consensus reaching process considering conflict relationship clustering in large-scale group decision-making problems

In large-scale group decision-making (LSGDM) events, conflicts among decision makers (DMs) usually occur, causing serious damage to the decision-making process. Accurate conflict detection and timely management in LSGDM can improve the efficiency of the consensus reaching process (CRP) and reduce the overall conflict degree. This paper presents a conflict management-based consensus reaching process (CM-CRP) to achieve centralized and efficient management of DMs. In CM-CRP, to further manage the conflict relationships among DMs, a new clustering algorithm is considered where DMs with conflicting opinions are clustered into a subgroup. To improve the accuracy of the management and precisely the portray behaviors of DMs, for a multi-attributes LSGDM event, the confidence of DMs at the attribute level is captured. Further, the dynamic opinion weight operator is proposed in CM-CRP combining the confidence level and conflict degree of DMs, which enables a more accurate measurement of DMs' receptivity to others' opinions. Comparative experiments prove that the proposed CM-CRP outperforms the existing CRP, and several simulations investigate the effect of different factors on the CM-CRP.