GRA method for probabilistic simplified neutrosophic MADM and application to talent training quality evaluation of segmented education

Article type: Research Article

Authors: Liu, Chang^{; *}

Affiliations: Jilin Business and Technology College, Changchun, Jilin, China

Correspondence: [*] Corresponding author. Chang Liu, Jilin Business and Technology College, Changchun, 130000, Jilin, China. E-mail: [email protected].

Abstract: The “3 + 2” segmented training between higher vocational colleges and applied undergraduate courses has opened up the rising channel of vocational education from junior college level to undergraduate level, and promoted the organic connection between higher vocational colleges and Universities of Applied Sciences. It is one of the important ways to establish a modern vocational education system. Exploring the monitoring mechanism of talent training quality is an important measure to ensure the achievement of the segmented training goal, and it is a necessary condition to successfully train high-quality skilled applied talents. The talent training quality evaluation of segmented education is viewed as multiple attribute decision-making (MADM) issue. In this paper, an extended probabilistic simplified neutrosophic number GRA (PSNN-GRA) method is established for talent training quality evaluation of segmented education. The PSNN-GRA method integrated with CRITIC method in probabilistic simplified neutrosophic sets (PSNSs) circumstance is applied to rank the optional alternatives and a numerical example for talent training quality evaluation of segmented education is used to proof the newly proposed method’s practicability along with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute.

Keywords: Multiple attributes decision making (MADM), probabilistic simplified neutrosophic sets (PSNSs), GRA method, talent training quality evaluation

DOI: 10.3233/JIFS-224494

Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8637-8647, 2023