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Neural Computing and Applications
Published in: Neural Computing and Applications 11/2018

08-03-2017 | Original Article

A multi-criteria decision-making method based on single-valued trapezoidal neutrosophic preference relations with complete weight information

Authors: Ru-xia Liang, Jian-qiang Wang, Hong-yu Zhang

Published in: Neural Computing and Applications | Issue 11/2018

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Abstract

Single-valued trapezoidal neutrosophic numbers (SVTNNs) have a strong capacity to depict uncertain, inconsistent, and incomplete information about decision-making problems. Preference relations represent a practical tool for presenting decision makers’ preference information regarding various alternatives. The purpose of this paper is to propose single-valued trapezoidal neutrosophic preference relations (SVTNPRs) as a strategy for tackling multi-criteria decision-making problems. First, this paper briefly reviews basic concepts about neutrosophic sets and SVTNNs and defines a new comparison method and new operations for SVTNNs. Next, two aggregation operators, the single-valued trapezoidal neutrosophic weighted arithmetic average operator and the single-valued trapezoidal neutrosophic weighted geometric average operator, are proposed for applications in information fusion. Then, this paper discusses the definitions of completely consistent SVTNPRs and acceptably consistent SVTNPRs. Finally, we outline a decision-making method based on SVTNPRs to address green supplier selection problems, and we conduct a comparison study and discussion to illustrate the rationality and effectiveness of the decision-making method.
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Metadata
Title
A multi-criteria decision-making method based on single-valued trapezoidal neutrosophic preference relations with complete weight information
Authors
Ru-xia Liang
Jian-qiang Wang
Hong-yu Zhang
Publication date
08-03-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 11/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2925-8

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