Multiple attribute decision-making method based on some normal neutrosophic Bonferroni mean operators. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Liu, Peide; Li, Honggang
• Is Part Of: Neural computing & applications. Volume 28:Issue 1(2017)
• Page Start: 179
• Page End: 194
• Publication Date: 2017-01
• Subjects: Multiple attribute decision making -- Normal neutrosophic numbers -- Normal neutrosophic Bonferroni mean aggregation operator; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2048-z
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract 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.