A Correlation-Based TOPSIS Method for Multiple Attribute Decision Making with Single-Valued Neutrosophic Information

Shouzhen Zeng, Dandan Luo (), Chonghui Zhang () and Xingsen Li
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Shouzhen Zeng: School of Business, Ningbo University, Ningbo 315211, P. R. China
Dandan Luo: School of Business, Ningbo University, Ningbo 315211, P. R. China
Chonghui Zhang: #x2020;College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, P. R. China
Xingsen Li: #x2021;Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou 510006, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2020, vol. 19, issue 01, 343-358

Abstract: The single-valued neutrosophic set (SVNS) is considered as an attractive tool for handling highly uncertain and vague information. With this regard, different from the most current distance-based technique for order preference by similarity to ideal solution (TOPSIS) methods, this study proposes a correlation-based TOPSIS model for addressing the single-valued neutrosophic (SVN) multiple attribute decision making (MADM) problems. To achieve this aim, we first develop a novel conception of SVN correlation coefficient, whose significant feature is that it lies in the interval [−1,1], which is in accordance with the classical correlation coefficient in statistics, whereas all the existing SVN correlation coefficients in the literature are within unit interval [0,1]. Afterwards, a weighted SVN correlation coefficient is also introduced to infuse the importance of attributes. Moreover, a correlation-based comprehensive index is further proposed to establish the central structure of TOPSIS model, called the SVN correlation-based TOPSIS approach. Finally, a numerical example and relevant comparative analysis are implemented to explain the applicability and effectiveness of the mentioned methodology.

Keywords: Single-valued neutrosophic set; TOPSIS; correlation coefficient; MADM (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1142/S0219622019500512

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