Affiliations: [a]
School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China
| [b]
Business School, University of Shanghai for Science and Technology, China
Correspondence:
[*]
Corresponding author. Shaojian Qu, School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China. E-mail: [email protected].
Abstract: In group decision-making (GDM), when decision-makers (DMs) feel it is unfair, they may take uncooperative measures to disrupt the consensus-reaching process (CRP). On the other hand, it is difficult for the moderator to objectively determine each DM’s unit consensus cost and weight in CRP. Hence, this paper proposes data-driven robust maximum fairness consensus models (RMFCMs) to address these. First, this paper uses the robust optimization method to construct multiple uncertainty sets to describe the uncertainty of the DMs’ unit adjustment cost and proposes the RMFCMs. Subsequently, based on the DMs’ historical data, the DMs’ weights in the CRP are determined by a data-driven method based on the kernel density estimation (KDE) method. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between governments and enterprises, and the experimental results verify the rationality and robustness of the proposed consensus model.
