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Article type: Research Article
Authors: Ren, Weijiaa; b | Du, Yuhonga; b; * | Sun, Ronglua; b | Du, Yuqinc | Lü, Mubod
Affiliations: [a] School of Mechanical Engineering, Tiangong University, Tianjin, China | [b] Tianjin Key Laboratory of Advanced Mechatronics Equipment Technology, Tiangong University, Tianjin, China | [c] School of Economics, University of Chinese Academy of Social Sciences, Beijing, China | [d] School of Foreign Languages, Beihang University, Beijing, China
Correspondence: [*] Corresponding author. Yuhong Du, E-mail: [email protected].
Abstract: To improve the accuracy of decision results in complex fuzzy environments, complex cubic fuzzy sets are studied, which can not only measure the periodicity of decision-making data, but also use interval values and single values to act together on the data. However, the fuzzy sets do not provide a reasonable explanation for some special cases of input arguments. Thus, the power average operator is used to eliminate the influence of extreme input arguments on decision results, and the Maclaurin symmetric mean operator considers the correlation between inputs in this paper. Firstly, we define the operation rules, distance measures, evaluation index function, and evaluation criteria in a complex cubic q-rung orthopair fuzzy environment. Then, some aggregation operators are proposed to aggregate complex cubic q-rung orthopair fuzzy numbers, and their desirable properties and some special cases are discussed. Next, we use the subjective and objective fusion method to determine the weight of attributes. Further, a multi-attribute decision-making method is established by combining aggregation operator, evaluation function, and weight determination method. Finally, the proposed method is applied to a specific quality evaluation problem, and the effectiveness and practicability of the proposed method are illustrated by other methods and parameter analysis.
Keywords: Complex cubic fuzzy set, aggregation operator, multi-attribute group decision-making, application
DOI: 10.3233/JIFS-230402
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3213-3231, 2023
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