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Article type: Research Article
Authors: Yao, Zhiganga | Ran, Huib; *
Affiliations: [a] Department of Economic Management, Sichuan College of Architectural Technology, Chengdu, China | [b] College of Finance and Logistics Management, Liuzhou Vocational & Technical College, Liuzhou, Guangxi, China
Correspondence: [*] Corresponding author. Hui Ran, College of Finance and Logistics Management, Liuzhou Vocational & Technical College, Liuzhou, 545006, Guangxi, China. E-mail: [email protected].
Abstract: At present, the basic pension insurance system for urban and rural residents in China has played a positive role in guaranteeing the basic life of the elderly in urban and rural areas. At present, the basic pension insurance system for urban and rural residents is not perfect, and there is still a great lag in the formulation of cross-system and cross-regional policies. There are differences in treatment between groups, between regions and between urban and rural areas. The coverage is not comprehensive enough and there are still some people who are not included in the basic protection system, etc. People urgently need a social pension insurance system that can provide reliable and sustainable protection in their old age. The operational efficiency evaluation of urban and rural residents’ basic pension insurance systems is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic numbers grey relational analysis (TFNN-GRA) method is built based on the traditional grey relational analysis (GRA) and triangular fuzzy neutrosophic sets (TFNSs). Finally, a numerical example for operational efficiency evaluation of urban and rural residents’ basic pension insurance systems has been given and some comparisons are used to illustrate advantages of 2TLNN-GRA method.
Keywords: Multiple attribute decision making (MAGDM) problems, triangular fuzzy neutrosophic sets (TFNSs), GRA method, operational efficiency evaluation
DOI: 10.3233/JIFS-221631
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9015-9026, 2023
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