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Heronian Mean Operator of Linguistic Neutrosophic Cubic Numbers and Their Multiple Attribute Decision-Making Methods

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  • Changxing Fan
  • Jun Ye

Abstract

Many aggregation operators in multiattribute decisions assume that attributes are independent of each other; this leads to an unreasonable situation in information aggregation and decision-making. Heronian mean is the aggregation operator that can embody the interaction between attributes. In this paper, we merge the linguistic neutrosophic cubic number (LNCN) and the Heronian mean operator together to develop a LNCN generalized weighted Heronian mean (LNCNGWHM) operator and a LNCN three-parameter weighted Heronian mean (LNCNTPWHM) operator and then discuss their properties. Further, two multiattribute decision methods based on the proposed LNCNGWHM or LNCNTPWHM operator are introduced under LNCN environment. Finally, an example is used to indicate the effectiveness of the developed methods.

Suggested Citation

  • Changxing Fan & Jun Ye, 2018. "Heronian Mean Operator of Linguistic Neutrosophic Cubic Numbers and Their Multiple Attribute Decision-Making Methods," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-13, July.
    • Handle: RePEc:hin:jnlmpe:4158264
    DOI: 10.1155/2018/4158264
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    Cited by:

    1. Changxing Fan & Sheng Feng & Keli Hu, 2019. "Linguistic Neutrosophic Numbers Einstein Operator and Its Application in Decision Making," Mathematics, MDPI, vol. 7(5), pages 1-12, April.
    2. Mei Tang & Jie Wang & Jianping Lu & Guiwu Wei & Cun Wei & Yu Wei, 2019. "Dual Hesitant Pythagorean Fuzzy Heronian Mean Operators in Multiple Attribute Decision Making," Mathematics, MDPI, vol. 7(4), pages 1-27, April.

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