Incorporating Intelligence in Multiple-Attribute Decision-Making using Algorithmic Framework and Double-Valued Neutrosophic Sets: Varied Applications to Employment Quality Evaluation for University Graduates

The evaluation of university employment quality is a comprehensive assessment of graduates' employment status and its alignment with market demands. This evaluation typically includes indicators such as employment rate, the relevance of job positions to majors, salary levels, job stability, and job satisfaction. By analyzing this data, universities can understand how graduates perform in the job market, assess the effectiveness of talent cultivation, and provide reference points for optimizing curriculum design, teaching methods, and industry-education integration. The employment quality evaluation for university graduates a MADM. Currently, Logarithmic TODIM (LogTODIM) and GRA techniques are employed to address MADM challenges. To handle uncertain data in this evaluation, the double-valued neutrosophic sets (DVNSs) have been introduced. This study constructs the double-valued neutrosophic number Logarithmic TODIM-GRA (DVNN-LogTODIM-GRA) technique to manage the MADM problem under DVNSs. To validate the proposed technique, a numerical study is conducted focusing on the employment quality evaluation for university graduates. The main contributions of this study are constructed: (1) The use of entropy to determine weight values under DVNSs; (2) The application of the DVNN-LogTODIM-GRA technique to effectively manage MADM; (3) The verification of the DVNN-LogTODIM-GRA technique through a numerical example related to the employment quality evaluation for university graduates.