Consistency and consensus-driven models to personalize individual semantics of linguistic terms for supporting group decision making with distribution linguistic preference relations
Consistency and consensus-driven models to personalize individual semantics of linguistic terms for supporting group decision making with distribution linguistic preference relations
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一致性和共识驱动的模型，用于个性化语言术语的个体语义，以支持具有分布语言偏好关系的群体决策
DOI：
10.1016/j.knosys.2019.105078
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发表时间：
2020-02
影响因子：
8.8
通讯作者：
Yang Shanlin
中科院分区：
文献类型：
--
作者：
Tang Xiaoan;Peng Zhanglin;Zhang Qiang;Pedrycz Witold;Yang Shanlin
分布语言偏好关系（DLPR）是一种借助多个语言项的概率分布对语言表达进行建模的方法，它为准确地获取决策者在语言决策中的偏好提供了一种有效的工具。同时，数值尺度模型已经成为话语标记语在解决语言决策问题时处理词汇计算的合适选择。本研究的重点是改善群体决策（GDM）与DLPR通过数字规模模型的帮助下，通过填补以下空白。很明显，单词可能对不同的人表现出不同的含义。在现实世界的模糊语言GDM中，DM可能对给定的语言术语有不同的理解。为每个DM设置个性化的语言术语语义成为具有DLPR的GDM中的关键任务。为此，我们首先定义了一个改进的数值尺度模型，以促进DLPR和数值模糊偏好关系之间的联系。然后分析了DLPR的加性一致性和乘性一致性，并给出了相应的一致性指标来度量DLPR的一致性水平。在此基础上，我们开发了两个一致性驱动的优化模型，以个性化的数字尺度的语言术语与个人DLPR。接下来，我们开发了一种使用DLPR解决GDM的方法。在所提出的方法中，设计了一个基于差异的共识措施。为了确定一组数字规模的语言术语与相应的组DLPR，两个一致性和共识驱动的优化模型。最后，通过实例分析验证了该方法的有效性和实用性.
Distribution linguistic preference relations (DLPRs) that model linguistic expressions with the aid of probabilistic distributions of multiple linguistic terms provide an effective tool to accurately elicit the preferences of decision makers (DMs) in linguistic decisions. Meanwhile, numerical scale models have been suitable choices for DMs to handle computing with words when solving linguistic decision problems. This study focuses on improving the group decision making (GDM) with DLPRs via the help of numerical scale models by filling the following gap. It is obvious that words might exhibit different meanings for different people. DMs may have a varying understanding of a given linguistic term in real-world fuzzy linguistic GDM. Setting personalized semantics of the linguistic terms for each DM becomes a critical task in GDM with DLPRs. To do this, we first define an improved numerical scale model to facilitate the linkages between DLPRs and numerical fuzzy preference relations. Then an additive consistency and a multiplicative consistency of DLPRs are analyzed, and the corresponding consistency indices are provided to measure the consistency levels of DLPRs. Based on them, we develop two consistency-driven optimization models to personalize numerical scales for linguistic terms with individual DLPRs. Next, we develop an approach for addressing GDM with DLPRs. In the proposed approach, a dissimilarity-based consensus measure is designed. To determine a group numerical scale for the linguistic terms with the corresponding group DLPR, two consistency and consensus-driven optimization models are constructed. Finally, illustrative examples are analyzed using the proposed approach to demonstrate its applicability and validity.
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影响因子：
11.8
作者：
Dong, Yucheng;Herrera-Viedma, Enrique
通讯作者：
Herrera-Viedma, Enrique
影响因子：
4.7
作者：
F. Herrera;S. Alonso;F. Chiclana;E. Herrera-Viedma
通讯作者：
F. Herrera;S. Alonso;F. Chiclana;E. Herrera-Viedma
DOI：
10.1016/j.cie.2003.12.012
发表时间：
2004-04
期刊：
Comput. Ind. Eng.
影响因子：
--
作者：
Z. Fan;Si-Han Xiao;G. Hu
通讯作者：
Z. Fan;Si-Han Xiao;G. Hu
影响因子：
11.9
作者：
F. Mata;Luis Martínez-López;E. Herrera-Viedma
通讯作者：
F. Mata;Luis Martínez-López;E. Herrera-Viedma
DOI：
10.1016/j.ins.2010.08.002
发表时间：
2011
期刊：
Inf. Sci.
影响因子：
--
作者：
Zeshui Xu;Xiaoqiang Cai
通讯作者：
Zeshui Xu;Xiaoqiang Cai