Decision-Making Approach Based on Generalized Aggregation Operators with Complex Single-Valued Neutrosophic Hesitant Fuzzy Set Information. (10th January 2022)

• Record Type: Journal Article
• Authors: Garg, Harish; Ali, Zeeshan; Hezam, Ibrahim M.; Gwak, Jeonghwan
• Other Names: Pamučar Dragan Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2022(2022)
• Publication Date: 2022-01-10
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2022/9164735
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : A strategic decision-making technique can help the decision maker to accomplish and analyze the information in an efficient manner. However, in our real life, an uncertainty will play a dominant role during the information collection phase. To handle such uncertainties in the data, we present a decision-making algorithm under the single-valued neutrosophic (SVN) environment. The SVN is a powerful way to deal the information in terms of three degrees, namely, "truth, " "falsity, " and "indeterminacy, " which all are considered independent. The main objective of this study is divided into three folds. In the first fold, we state the novel concept of complex SVN hesitant fuzzy (CSVNHF) set by incorporating the features of the SVN, complex numbers, and the hesitant element. The various fundamental and algebraic laws of the proposed CSVNHF set are described in details. The second fold is to state the various aggregation operators to obtain the aggregated values of the considered CSVNHF information. For this, we stated several generalized averaging operators, namely, CSVNHF generalized weighted averaging, ordered weighted average, and hybrid average. The various properties of these operators are also stated. Finally, we discuss a multiattribute decision-making (MADM) algorithm based on the proposed operators to address the problems under the CSVNHF environment. A numerical example is given to illustrate the work and compare the results with the existing studies' results. Also, the sensitivity analysis and advantages of the stated algorithm are given in the work to verify and strengthen the study.