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Generalized Ordered Weighted Simplified Neutrosophic Cosine Similarity Measure for Multiple Attribute Group Decision Making

Generalized Ordered Weighted Simplified Neutrosophic Cosine Similarity Measure for Multiple Attribute Group Decision Making

Jun Ye
Copyright: © 2020 |Volume: 14 |Issue: 1 |Pages: 12
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799805311|DOI: 10.4018/IJCINI.2020010104
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MLA

Ye, Jun. "Generalized Ordered Weighted Simplified Neutrosophic Cosine Similarity Measure for Multiple Attribute Group Decision Making." IJCINI vol.14, no.1 2020: pp.51-62. http://doi.org/10.4018/IJCINI.2020010104

APA

Ye, J. (2020). Generalized Ordered Weighted Simplified Neutrosophic Cosine Similarity Measure for Multiple Attribute Group Decision Making. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 14(1), 51-62. http://doi.org/10.4018/IJCINI.2020010104

Chicago

Ye, Jun. "Generalized Ordered Weighted Simplified Neutrosophic Cosine Similarity Measure for Multiple Attribute Group Decision Making," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 14, no.1: 51-62. http://doi.org/10.4018/IJCINI.2020010104

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Abstract

The paper proposes a generalized ordered weighted simplified neutrosophic cosine similarity (GOWSNCS) measure by combining the cosine similarity measure of simplified neutrosophic sets (SNSs) with the generalized ordered weighted averaging (GOWA) operator and investigates its properties and special cases. Then, the author develops a simplified neutrosophic group decision-making method based on the GOWSNCS measure to handle multiple attribute group decision-making problems with simplified neutrosophic information. The prominent characteristics of the GOWSNCS measure are that it not only is a generalization of the cosine similarity measure but also considers the associated weights for attributes and decision makers in the aggregation of the cosine similarity measures of SNSs to alleviate the influence of unduly large or small similarities in the process of information aggregation. Finally, an illustrative example of investment alternatives is provided to demonstrate the application and effectiveness of the developed approach.