Abstract
The confidence levels can reduce the influence of the unreasonable evaluation value that was given by the decision-maker on the decision-making results. The Archimedean t-norm and t-conorm (ATS) also have many advantages for the processing of uncertain data. Under this environment, the confidence q-rung orthopair fuzzy aggregation operator based on ATS is one of the most successful extensions of confidence q-rung orthopair fuzzy numbers in which we decrease the deviation caused by the subjective perspective of the decision-maker in the multicriteria group decision-making problems. In this paper, we propose weighted, ordered weighted averaging aggregation operators and weighted, ordered weighted geometric aggregation operators based on ATS, respectively. Moreover, the properties and four specific forms associated with aggregation operators are also investigated. In this study, a novel MCGDM approach is introduced by using the proposed operator. A reasonable example is proposed and compared the results that are obtained by our operators and that in the existing literature, so as to verify the rationality and flexible of our method. From the study, we concluded that the proposed method can reduce the impact of extreme data, and make decision-making results more reasonable by considering the attitudes of decision-makers.
Highlights
In recent years, due to the complexity of objective things, uncertainty and the fuzziness of cognitive-based human thinking, the research on multi-attribute decision making (MCGDM) problems in uncertain environment has attracted great attention of scholars
In order to solve this problem, Atanassov [1, 2] proposed intuitionistic fuzzy sets (IFSs), which have investigated from three aspects: membership degree (MD), nonmembership degree (NMD) and hesitancy degree (HD)
Liu and Chen’s method [23] can’t deal with such data, whereas the method we proposed is based on the fact that is q-rung orthopair fuzzy number (q-ROFN), which can flexibly adjust the parameter value q according to the requires of data in solving various uncertainty problems, so the proposed theory can be more widely applied and more effectively solve such problems
Summary
Due to the complexity of objective things, uncertainty and the fuzziness of cognitive-based human thinking, the research on multi-attribute decision making (MCGDM) problems in uncertain environment has attracted great attention of scholars. Because the decision-making body is generally human, the evaluation value is usually uncertain and vague, which requires a reasonable tool to express it. Many researches with regard to IFSs have emerged and applied in medical diagnosis [35], pattern recognition [6] and group decision-making (GDM) [18]. These studies mainly carried out from five different fields
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