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Neural Computing and Applications
Published in: Neural Computing and Applications 10/2019

25-04-2018 | Original Article

Deriving the priority weights from multiplicative consistent single-valued neutrosophic preference relations

Authors: Na Wang, Fanyong Meng, Yanwei Xu

Published in: Neural Computing and Applications | Issue 10/2019

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Abstract

Preference relation is one of the most important and relatively simple approaches to address decision-making problems. As a special case of neutrosophic sets, the single-valued neutrosophic set (SVNS) is an efficient and powerful tool to deal with imprecise and inconsistent information. Taking the advantages of preference relations and SVNSs, this paper introduces single-valued neutrosophic preference relations (SVNPRs). Just as other types of preference relations, consistency and consensus analyses are indispensable to guarantee the ranking order logically. A multiplicative consistency concept for SVNPRs is introduced. On the basis of this concept, several multiplicative consistency-based 0–1 mixed programming models are established to derive multiplicatively consistent SVNPRs and estimate missing values in incomplete SVNPRs, respectively. As for group decision-making, a distance measure-based consensus index is presented. Moreover, an algorithm for group decision-making with SVNPRs is developed, which can deal with incomplete and inconsistent SVNPRs. Finally, a practical example is offered to show the practicability of the new procedure, and comparison is made with several previous methods about decision-making with SVNSs.
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Metadata
Title
Deriving the priority weights from multiplicative consistent single-valued neutrosophic preference relations
Authors
Na Wang
Fanyong Meng
Yanwei Xu
Publication date
25-04-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3493-2

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