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Article type: Research Article
Authors: Zhai, Shanshana | Fan, Jianpingb | Liu, Lina; *
Affiliations: [a] Shijiazhuang Posts and Telecommunications Technical College, Shijiazhuang, China | [b] School of Economics and Management, Shanxi University, Taiyuan, China
Correspondence: [*] Corresponding author. Lin Liu, Shijiazhuang Posts and Telecommunications Technical College, Shijiazhuang 050022, China. E-mail: [email protected].
Abstract: Neutrosophic cubic set (NCS) can process complex information by choosing both interval value and single value membership and indeterminacy and falsehood components. The aggregation operators based on Aczel-Alsina t-norm and t-corm are quite effective for evaluating the interrelationship among attributes. The purpose of this paper is to diagnose the interrelationship among attributes with neutrosophic cubic information, and propose a multi-attribute decision-making(MADM) method for supplier selection problem with unknown weight under neutrosophic cubic environment. We defined neutrosophic cubic Aczel-Alsina (NC-AA) operator and neutrosophic cubic Aczel–Alsina weighted arithmetic average (NCAAWAA) operator, then we discussed various important results and some properties of the proposed operators. Additionally, we proposed a MADM method under the presence of the NC-AAWAA operator. When the weights of attributes are unknown, we use the MEREC method to determine the weights. Later, the NC-AAWAA operator and MEREC method are applied to address the supplier selection problem. Finally, a sensitivity analysis and a comparative analysis are conducted to illustrate the stability and superiority of the proposed method. The results show the NC-AAWAA operator can handle the interrelationship among complex information more effectively, and MEREC method can weight the attributes based on the removal effect of a neutrosophic cubic attribute.
Keywords: Multi-attribute decision-making (MADM), neutrosophic cubic set (NCS), Aczel-Alsina aggregation operators, MEREC method
DOI: 10.3233/JIFS-235274
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
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