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

Abstract

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

DOI: 10.5281/zenodo.13988798