The 360 degree feedback evaluation method is a multidimensional, comprehensive assessment method. Evaluators may hesitate among multiple evaluation values and be simultaneously constrained by the biases and cognitive errors of the evaluators, evaluation results are prone to unfairness and conflicts. To overcome these issues, this paper proposes a consensus-based 360 degree feedback evaluation method with linguistic distribution assessments. Firstly, evaluators provide evaluation information in the form of linguistic distribution. Secondly, utilizing an enhanced ordered weighted averaging (OWA) operator, the model aggregates multi-source evaluation information to handle biased evaluation information effectively. Subsequently, a consensus-reaching process is established to coordinate conflicting viewpoints among the evaluators, and a feedback adjustment mechanism is designed to guide evaluators in refining their evaluation information, facilitating the attainment of a unanimous evaluation outcome. Finally, the improved 360 degree feedback evaluation method was applied to the performance evaluation of the project leaders in company J, thereby validating the effectiveness and rationality of the method.

1. Conflicts frequently arise from the divergent interests and objectives of the decision makers (DMs) (Bai et al., 2016; Duleba & Blahota, 2022; Kress, 2012; Roehrich et al., 2025; Sant’anna et al...

The inverse graph model for conflict resolution (GMCR) aims to help stakeholders achieve the optimal preferences that can make the desired state an equilibrium. Common interests often drive stakeholders to form interest coalitions. Existing studies on inverse GMCR are primarily conducted in numerical environments and tend to overlook the consensus issues within interest coalitions. In reality, however, preference divergences arising from stakeholders’ diverse knowledge backgrounds often hinder interest coalitions from achieving greater common benefits, and their decision-making processes typically take place in linguistic environments characterized by personalized individual semantics (PISs). To address this, this study proposes a consensus-based inverse GMCR that incorporates PISs, and employs linguistic distribution assessments (LDAs) to represent stakeholders’ preferences. Specifically, this study first introduces a consensus-driven PIS model with historical data, which aims to address stakeholders’ PISs by utilizing historical conflict data of interest coalitions. Subsequently, a minimum-adjustment-based inverse preference optimization model with bounded confidence is proposed to assist stakeholders in attaining optimal preferences that render the desired state an equilibrium. By incorporating minimum adjustment and bounded confidence, the proposed model reduces adjustment costs and enhances stakeholders’ willingness to revise their preferences. At the same time, the model ensures both group consensus among stakeholders and individual consistency. Finally, the feasibility and effectiveness of the proposed method are demonstrated through a hypothetical case, sensitivity analysis, comparative analysis, and a discussion.