Managing personalized individual semantics and consensus in linguistic distribution large-scale group decision making

Abstract A linguistic distribution assessment is an effective approach to represent uncertain preferences in large-scale group decision making (LSGDM). The same word often signifies different things for different decision makers in linguistic distribution assessments, which is called personalized individual semantics (PIS). Moreover, preference conflicts widely exist in LSGDM. This paper develops a new framework to address PIS and consensus in LSGDM using linguistic distribution preference relations (LDPRs). In the proposed LSGDM framework, a consistency-driven optimization model is put forward to produce the numerical scales with the PIS by maximizing the consistency of the additive preference relation that is transformed from its LDPR. Then, a preference clustering technique is employed to decompose decision makers into different clusters for managing their preferences. Next, the paper devises a two-stage-based consensus reaching model to manage the individual consistency and group consensus, which seeks to minimize the preference information loss. The first stage aims to assist decision makers in achieving a consensus within each obtained cluster, and the second stage is devoted to facilitating the consensus building among the different clusters. Finally, a case study that evaluates water management plans and a comparative analysis with the existing baseline approach are conducted to assess the feasibility and validity of the proposed LSGDM framework.