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Patent quality assessment is a multi-attribute decision-making problem. The increasingly complex social environment makes group decision-making based on interval-valued intuitionistic fuzzy (IVIF) information more and more important. In group decision-making, considering using a comprehensive projection measure to calculate the similarity between two decision matrices is one of the important steps. In order to make up for the deficiencies of the classical projection measure and the existing normalized projection measure in measuring the similarity between two IVIF vectors and optimize the group decision-making performance of the VIKOR method, this study proposes: (1) a new definition of subtraction of IVIF numbers, which further enriches the theoretical basis of IVIF numbers; (2) a new normalized projection measure to measure the similarity between two IVIF matrices; (3) combined with the regret matrix to store group regret information, which enhances the comprehensive ability of the classical VIKOR technique, and then establishes a comprehensive GDM method for patent quality assessment. Finally, a specific patent quality assessment example in the existing literature is used for data experiment. Through static and dynamic experimental comparative analysis, the feasibility and effectiveness of the proposed method are illustrated, and it can almost produce a stable ranking. The research in this paper not only provides an effective group decision-making tool for patent quality assessment, but also provides theoretical support and practical guidance for fuzzy group decision-making problems in other fields.
Xie et al. (Tue,) studied this question.