详细信息
Multi-Criteria Decision-Making Method Using Heronian Mean Operators under a Bipolar Neutrosophic Environment ( SCI-EXPANDED收录) 被引量:17
文献类型:期刊文献
英文题名:Multi-Criteria Decision-Making Method Using Heronian Mean Operators under a Bipolar Neutrosophic Environment
作者:Fan, Changxing[1];Ye, Jun[2];Feng, Sheng[1];Fan, En[1];Hu, Keli[1]
机构:[1]Shaoxing Univ, Dept Comp Sci, 508 Huancheng West Rd, Shaoxing 312000, Peoples R China;[2]Shaoxing Univ, Dept Elect & Informat Engn, 508 Huancheng West Rd, Shaoxing 312000, Peoples R China
年份:2019
卷号:7
期号:1
外文期刊名:MATHEMATICS
收录:SCI-EXPANDED(收录号:WOS:000459734200096)、、Scopus(收录号:2-s2.0-85060216549)、WOS
基金:This research was funded by [the National Natural Science Foundation of China] grant number [61703280, 61603258], [the Science and Technology Planning Project of Shaoxing City of China] grant number [2017B70056, 2018C10013], [the General Research Project of Zhejiang Provincial Department of Education] grant number [Y201839944], and [the Public Welfare Technology Research Project of Zhejiang Province] grant number [LGG19F020007].
语种:英文
外文关键词:bipolar neutrosophic number (BNN); BNN improved generalized weighted HM (BNNIGWHM) operator; BNN improved generalized weighted geometry HM (BNNIGWGHM) operator; decision-making
外文摘要:In real applications, most decisions are fuzzy decisions, and the decision results mainly depend on the choice of aggregation operators. In order to aggregate information more scientifically and reasonably, the Heronian mean operator was studied in this paper. Considering the advantages and limitations of the Heronian mean (HM) operator, four Heronian mean operators for bipolar neutrosophic number (BNN) are proposed: the BNN generalized weighted HM (BNNGWHM) operator, the BNN improved generalized weighted HM (BNNIGWHM) operator, the BNN generalized weighted geometry HM (BNNGWGHM) operator, and the BNN improved generalized weighted geometry HM (BNNIGWGHM) operator. Then, their propositions were examined. Furthermore, two multi-criteria decision methods based on the proposed BNNIGWHM and BNNIGWGHM operator are introduced under a BNN environment. Lastly, the effectiveness of the new methods was verified with an example.
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