Multiple attribute decision-making method based on some normal neutrosophic Bonferroni mean operators

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• 作者：Peide Liu ; Honggang Li
• 关键词：Multiple attribute decision making ; Normal neutrosophic numbers ; Normal neutrosophic Bonferroni mean aggregation operator
• 刊名：Neural Computing and Applications
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：28
• 期：1
• 页码：179-194
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Visi
• 出版者：Springer London
• ISSN：1433-3058
• 卷排序：28

文摘

Normal neutrosophic numbers (NNNs) are a significant tool of describing the incompleteness, indeterminacy, and inconsistency of the decision-making information. In this paper, we firstly propose the definition and the properties of the NNNs, and the accuracy function, the score function, and the operational laws of the NNNs are developed. Then, some operators are presented, including the normal neutrosophic Bonferroni mean operator, the normal neutrosophic weighted Bonferroni mean (NNWBM) operator, the normal neutrosophic geometric Bonferroni mean operator, and the normal neutrosophic weighted geometric Bonferroni mean (NNWGBM) operator. We also study their properties and special cases. Further, we put forward a multiple attribute decision-making method which is based on the NNWBM and NNWGBM operators. Finally, an illustrative example is given to verify the practicality and validity of the proposed method.