CODAS method for Pythagorean 2-tuple linguistic MAGDM and applications to quality evaluation of scientific research management in application-oriented universities

Article type: Research Article

Authors: Li, Hui^{; *}

Affiliations: Suqian University, Suqian, Jiangsu, China

Correspondence: [*] Corresponding author. Hui Li, Suqian University, Suqian, 223800, Jiangsu, China. E-mail: [email protected].

Abstract: The scientific research work of colleges and universities has attracted more and more social attention because of its large number of multidisciplinary scientific and technological talents, hardware facilities and good scientific research environment, and the quality of scientific and technological management work of colleges and universities directly affects the level of scientific and technological work of colleges and universities. Starting from the common problems of scientific research management in colleges and universities, this paper explores the ideas and methods to further promote scientific research work by improving the quality of scientific research management. The quality evaluation of scientific research management in application-oriented universities is classical multiple attribute group decision making (MAGDM). Based on this, we extend the traditional CODAS method to the Pythagorean 2-tuple linguistic sets (P2TLSs) and propose the Pythagorean 2-tuple linguistic CODAS (P2TL-CODAS) method for quality evaluation of scientific research management in application-oriented universities. The P2TL-CODAS method is established and all computing steps are simply presented. Furthermore, we apply the P2TL-CODAS method to evaluate the quality evaluation of scientific research management in application-oriented universities.

Keywords: Multiple attribute group decision making (MAGDM), Pythagorean 2-tuple linguistic sets (P2TLSs), CODAS method, P2TL-CODAS model, quality evaluation of scientific research management

DOI: 10.3233/JIFS-230629

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1833-1845, 2023