Enhancing Multiple-Attribute Decision-Making Framework for Talent Cultivation Quality Evaluation Through Using Weighted Dual Hamy Mean and Prioritized Aggregation Operators

Abstract

: With the rapid development of the economy and the transformation and upgrading of 
industrial structure, universities have increased their focus on theoretical knowledge and 
application, actively providing practical platforms for students, enriching the connection 
between theoretical knowledge and practice, deepening the reform of quality education, 
constructing a modern education system, and ensuring that students can adapt their 
professional skills to the requirements of industry technology in the field of computer
information. Cultivating high-quality computer information talents can not only promote better 
employment for students, but also promote the development of computerinformation education 
in universities. The talent cultivation quality evaluation in practical aspects of computer
information major is looked as the multiple-attribute decision-making (MADM) issue. In this 
paper, we extended the weighted dual Hamy mean (WDHM) operator and prioritized 
aggregation (PA) operator to 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple 
linguistic neutrosophic prioritized WDHM (2TLNPWDHM) operator. Finally, a numerical
example for talent cultivation quality evaluation in practical aspects of computer information 
major is employed to verify the 2TLNPWDHM operator. The main contributions of this study 
are summarized: (1) the 2TLNPWDHM operator is built; (2) the 2TLNPWDHM operator is 
designed to cope with the MADM with 2TLNNs; (3) a numerical example for talent cultivation 
quality evaluation in practical aspects of computer information major is supplied to proof the 
designed method; (4) some comparative studies are utilized to verify the rationality of the 
2TLNPDHM

DOI: 10.5281/zenodo.13935248