A minimum cost and maximum fairness-driven multi-objective optimization consensus model for large-scale group decision-making

The rise of social media and e-democracy has driven a paradigm shift in decision-making, notably reflected in the changing ways of public participation and policymaking within decision- making processes. The increased focus on fairness and efficiency not only complicates the consensus-building process among diverse interests and perspectives, but also significantly adds to the complexity of decision-making and its implementation. In this context, balancing the interests of all parties while ensuring fairness and improving decision-making effectiveness becomes crucial. To address these challenges, this study develops a multi-objective optimization consensus framework for large-scale group decision-making (LSGDM) that integrates the interests of decision-makers (DMs) and a moderator, providing a more comprehensive tool. Specifically, this study first designs a DM weight determination method based on structural hole theory within fuzzy social networks. The proposed DM weight determination method effectively leverages the flexibility of fuzzy social networks and the comprehensiveness of structural hole theory to enhance the accuracy and reliability of weight assignment. Building on this, a novel clustering method based on the maximum group consensus level is developed, taking into account the varying importance of different DMs. Furthermore, a minimum cost and maximum fairness-driven multi- objective optimization LSGDM consensus model, referred to as MCMF-MO-LSGDM, is explored in this study. Finally, the utility and superiority of the constructed model are confirmed through comparative analysis and simulation experiments against existing related works.