文献类型：期刊文献

英文题名：Heronian mean operators of linguistic neutrosophic multisets and their multiple attribute decision-making methods

作者：Fan, Changxing[1];Hu, Keli[1];Feng, Sheng[1];Ye, Jun[2];Fan, En[1]

机构：[1]Shaoxing Univ, Dept Comp Sci, 508 Huancheng West Rd, Shaoxing 312000, Peoples R China;[2]Shaoxing Univ, Elect & Informat Engn Dept, Shaoxing, Peoples R China

年份：2019

卷号：15

期号：4

外文期刊名：INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS

收录：SCI-EXPANDED(收录号：WOS:000464967200001)、、EI(收录号：20191706823601)、Scopus(收录号：2-s2.0-85064553768)、WOS

基金：The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the National Natural Science Foundation of China grant number (61603258), (61703280); General Research Project of Zhejiang Provincial Department of Education grant number (Y201839944); Public Welfare Technology Research Project of Zhejiang Province grant number (LGG19F020007); Public Welfare Technology Application Research Project of Shaoxing City grant number (2018C10013).

语种：英文

外文关键词：Neutrosophic multiset; linguistic neutrosophic multiset; linguistic neutrosophic multiplicity number; linguistic neutrosophic multiplicity number generalized-weighted Heronian mean operator; linguistic neutrosophic multiplicity number improved-generalized-weighted Heronian mean operator; multi attributes decision-making

外文摘要：A valid aggregation operator can reflect the decision result more clearly and make the decision effect more correctly. In this article, a linguistic neutrosophic multiset is first proposed to handle the multiplicity information, which is an expanding of neutrosophic multiset. Two Heronian mean operators are proposed to aggregate the linguistic neutrosophic multiset, one is a linguistic neutrosophic multiplicity number generalized-weighted Heronian mean operator, the other is a linguistic neutrosophic multiplicity number improved-generalized-weighted Heronian mean operator, and then their properties are discussed. Furthermore, two decision-making methods are introduced based on linguistic neutrosophic multiplicity number generalized-weighted Heronian mean or linguistic neutrosophic multiplicity number improved-generalized-weighted Heronian mean operators under linguistic neutrosophic multiplicity number environment. Finally, an illustrative example is used to indicate the practicality and validity of these two methods.