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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2018

17.11.2016 | Original Article

Multi-criteria group decision-making method based on interdependent inputs of single-valued trapezoidal neutrosophic information

verfasst von: Ru-xia Liang, Jian-qiang Wang, Lin Li

Erschienen in: Neural Computing and Applications | Ausgabe 1/2018

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Abstract

Single-valued trapezoidal neutrosophic numbers (SVTNNs) are very useful tools for describing complex information, because they are able to maintain the completeness of the information and describe it accurately and comprehensively. This paper develops a method based on the single-valued trapezoidal neutrosophic normalized weighted Bonferroni mean (SVTNNWBM) operator to address multi-criteria group decision-making (MCGDM) problems. First, the limitations of existing operations for SVTNNs are discussed, after which improved operations are defined. Second, a new comparison method based on score function is proposed. Then, the entropy-weighted method is established in order to obtain objective expert weights, and the SVTNNWBM operator is proposed based on the new operations of SVTNNs. Furthermore, a single-valued trapezoidal neutrosophic MCGDM method is developed. Finally, a numerical example and comparison analysis are conducted to verify the practicality and effectiveness of the proposed approach.
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Metadaten
Titel
Multi-criteria group decision-making method based on interdependent inputs of single-valued trapezoidal neutrosophic information
verfasst von
Ru-xia Liang
Jian-qiang Wang
Lin Li
Publikationsdatum
17.11.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2672-2

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