School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan Shandong, China.
PLoS One. 2018 Mar 7;13(3):e0193027. doi: 10.1371/journal.pone.0193027. eCollection 2018.
Linguistic neutrosophic numbers (LNNs) can easily describe the incomplete and indeterminate information by the truth, indeterminacy, and falsity linguistic variables (LVs), and the Hamy mean (HM) operator is a good tool to deal with multiple attribute group decision making (MAGDM) problems because it can capture the interrelationship among the multi-input arguments. Motivated by these ideas, we develop linguistic neutrosophic HM (LNHM) operator and weighted linguistic neutrosophic HM (WLNHM) operator. Some desirable properties and special cases of two operators are discussed in detail. Furthermore, considering the situation in which the decision makers (DMs) can't give the suitable weight of each attribute directly from various reasons, we propose the concept of entropy for linguistic neutrosophic set (LNS) to obtain the attribute weight vector objectively, and then the method for MAGDM problems with LNNs is proposed, and some examples are used to illustrate the effectiveness and superiority of the proposed method by comparing with the existing methods.
语言型 Neutrosophic 数 (LNNs) 可以通过真实、不确定和虚假语言变量 (LVs) 轻松描述不完整和不确定的信息，而 Hamy 均值 (HM) 算子是处理多属性群决策问题 (MAGDM) 的一种很好的工具，因为它可以捕捉到多输入参数之间的相互关系。受这些思想的启发，我们开发了语言型 Neutrosophic HM (LNHM) 算子和加权语言型 Neutrosophic HM (WLNHM) 算子。详细讨论了这两个算子的一些理想性质和特殊情况。此外，考虑到决策者 (DMs) 由于各种原因无法直接给出每个属性的适当权重的情况，我们提出了语言型 Neutrosophic 集 (LNS) 的熵的概念来客观地获得属性权重向量，然后提出了使用 LNNs 的 MAGDM 问题的方法，并通过与现有方法进行比较，使用一些示例来说明所提出方法的有效性和优越性。
