ELICIT information-based robust large-scale minimum cost consensus model under social networks

Large-Scale Group Decision-Making (LSGDM) in social network context has emerged as a research focus in decision sciences. Social relationships implicated in the network influence Decision-Makers' (DMs) preferences and group consensus. However, existing research often overlooks the potential impact of uncertain adjustment costs driven by social relationships among DMs on the Consensus-Reaching Process (CRP). To address this issue, this paper develops a new Extended Linguistic Expressions with Symbolic Translation (ELICIT) information- based robust large-scale minimum cost consensus model under social networks. Firstly, the ELICIT model is used to represent DMs' preferences, enhancing preference elicitation under uncertain conditions. Secondly, DMs' weights are objectively determined based on the following-follower network, and the social network cost function is integrated into the Comprehensive Minimum Cost Consensus (CMCC) model. Then, three robust consensus models are developed to manage the uncertain adjustment costs of DMs within the network. Afterward, an ELICIT-based PROMETHEE ranking method is designed. Finally, a case study on selecting Healthcare Waste (HCW) treatment technology is conducted. The implemented sensitivity and comparative analysis demonstrate the effectiveness and advantages of the proposed method.