Managing Multigranular Unbalanced Hesitant Fuzzy Linguistic Information in Multiattribute Large-Scale Group Decision Making: A Linguistic Distribution-Based Approach
Managing Multigranular Unbalanced Hesitant Fuzzy Linguistic Information in Multiattribute Large-Scale Group Decision Making: A Linguistic Distribution-Based Approach
复制标题
管理多属性大规模群体决策中的多粒度不平衡犹豫模糊语言信息：基于语言分布的方法
DOI：
10.1109/tfuzz.2019.2949758
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发表时间：
2020-11
影响因子：
11.9
通讯作者：
Gao Yuan
中科院分区：
文献类型：
--
作者：
Zhang Zhen;Yu Wenyu;Martinez Luis;Gao Yuan
随着技术和社会需求的不断增长，越来越多的决策者参与到群体决策过程中，这就是所谓的大规模群体决策。对于具有语言信息的LGDM问题，由于知识和文化背景的差异，不同的决策者倾向于提供定义在多粒度语言项集上的语言评估，并且决策者使用犹豫模糊语言项集（HFLTSs）来模拟其评估的犹豫。本文首先提出了一种算法来表示语言分布评估（LDA）使用犹豫的语言分布（HLD）。另外两个算法，然后提出了一个不平衡的HFLTS转换成一个平衡的LDA和一个平衡的LDA转换成一个不平衡的LDA，分别。在此基础上，提出了一种处理多粒度不平衡犹豫模糊语言信息的多属性LGDM问题的方法。在该方法中，所有不平衡的犹豫模糊语言信息被转换成LDA定义在一个平衡的语言术语集，然后基于LDA的聚类算法设计的聚类决策者。在聚类结果的基础上，进一步融合决策者的语言分布决策矩阵，得到对方案的集体评价。为了给决策者提供易于理解的语言结果，所有备选方案的LDA都由定义在每个决策者的初始语言项集上的HLD表示。最后，以地铁线路选择为例进行了验证.
With the increase of technological and societal demands, more and more decision makers are involved in the process of group decision making, which is called large-scale group decision making (LGDM). For an LGDM problem with linguistic information, it is common that different decision makers tend to provide linguistic assessments defined on multigranular linguistic term sets due to the difference in knowledge and culture background, and that hesitant fuzzy linguistic term sets (HFLTSs) are used by decision makers to model the hesitancy of their assessments. This article proposes first an algorithm to represent a linguistic distribution assessment (LDA) using a hesitant linguistic distribution (HLD). Two other algorithms are then proposed to transform an unbalanced HFLTS into a balanced LDA and to transform a balanced LDA into an unbalanced LDA, respectively. An approach is then proposed to deal with multiattribute LGDM problems with multigranular unbalanced hesitant fuzzy linguistic information based on these algorithms. In the proposed approach, all unbalanced hesitant fuzzy linguistic information is transformed into LDAs defined on a balanced linguistic term set, and then an LDA-based clustering algorithm is devised to cluster decision makers. Based on the clustering result, decision makers’ linguistic distribution decision matrices are further fused to obtain collective assessments of alternatives. In order to provide easy-to-understand linguistic results for decision makers, all LDAs of alternatives are represented by HLDs defined on each decision maker's initial linguistic term set. Finally, an example for the selection of subway lines is used to demonstrate the proposed approach.
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影响因子：
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DOI：
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