Two-stage multi-attribute reviewer-paper matching decision-making in a Fermatean fuzzy environment

The purpose of this paper is to propose a new two-stage one-to-two matching decision-making method in a Fermatean fuzzy environment to make up for limitations of reviewer-paper matching research. The research framework is developed in two phases: in the first stage, according to Fermatean fuzzy evaluation matrices of reviewers with respect to the attribute set, the editor of journals uses the grey correlation method to select reviewers. In the second stage, positive and negative ideal points of two-sided agents are calculated to construct positive and negative prospect matrices and income and loss matrices; on this basis, comprehensive positive and negative prospect matrices are obtained. Subsequently, a single-objective programming model based on the idea of maximizing deviation and linear inequality constraints is established to determine attribute weights; then, a positive similarity matrix, a negative similarity matrix, and a fairness matrix are calculated. Furthermore, a one-to-two matching model between papers and reviewers is established, and the optimal matching scheme is acquired by solving the model. Finally, the practicability of the proposed method is verified by an example analysis of reviewer-paper matching, effectively addressing the challenges of information uncertainty and bilateral satisfaction optimization in the review process. Sensitivity testing and comparative analysis further confirm the superiority of the proposed method over conventional ones.