A Promising Approach For Decision Modeling With CODAS Method For the Confidence Levels 2-tuple linguistic Complex q-Rung Orthopair Fuzzy Information

To finding appropriate recycling methods for plastic materials is a critical research issue and the effectiveness of a new method is being tested in this problem. Decision-making under conditions of ambiguity can be difficult due to the lack of understanding and incomplete information. The objective...

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Bibliographic Details
Published inIEEE access Vol. 11; p. 1
Main Authors Ullah, Irfan, Ghani, Fazal, Abdullah, Saleem, Khan, Faisal, Khan, Saifullah
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Summary:To finding appropriate recycling methods for plastic materials is a critical research issue and the effectiveness of a new method is being tested in this problem. Decision-making under conditions of ambiguity can be difficult due to the lack of understanding and incomplete information. The objective of this study is to enhance the process of selecting the most suitable recycling method. To address uncertainty in group decision-making, this essay proposes using MAGDM approaches with 2-tuple linguistic confidence level complex q-rung orthopair fuzzy sets (2TLCLCq-ROFSs). The CODAS method is extended to 2TLCLCq-ROFSs to tackle MAGDM issues. The 2TLCLCq-ROFSs is an innovative fuzzy framework that confirms positive and negative degrees on the unit circle of the complex plane to resolve ambiguity and vague data effectively. Using the innovative concept of a 2-tuple linguistic Cq-ROFS with confidence level , we presented a novel decision-making approach in this study (2TLCLCq-ROFSs). Additionally,we also perform a comparative sensitivity analysis to examine the results of CODAS method and compare it by some existing MCDM methods. These analyses show that the proposed method is efficient, and the results are also shown graphically in this work.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3286540