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

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 computer information 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.