Group Consensus Optimization Ranking Approach with Expert Reliability and Social Network Relationships

In group decision making, reaching consensus of decision groups is important to obtain reliable decision-making result. Moreover, expert reliability can significantly affect the result reliability, and unreliable experts provide incorrect information and affect the rationality and effectiveness of decision result. A few studies have considered expert reliability and defined it as the similarity of each expert's opinions before and after adjustment in the iterative consensus process. They have not considered how to eliminate overly unreliable experts before consensus process to improve the reliability of result, or how to define and measure expert reliability in non-iterative optimization consensus process. To deal with these issues, this paper proposes a group consensus optimization ranking approach with expert reliability and social network relationships to avoid the negative impact of unreliable experts on decision-making. The similarity between original and consistent preference is used to measure expert reliability, the weights of experts are obtained by social network relationships. When the reliability of some experts doesn't meet the requirement, they should be excluded before consensus process. For the remaining experts, an improved consensus optimization model and a median ranking model are established to promote consensus and obtain final ranking result based on a new ordinal consensus measurement. Finally, a numerical example about pollution control plan selection and a comparison analysis are given to illustrate the feasibility and advantage of the proposed approach.