A Consensus Model for Group Decision Making with Hesitant Fuzzy Information

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Zhiming Zhang, Chao Wang, Xuedong Tian
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引用次数: 6

Abstract

This article presents a more improved consensus-based method for dealing with multi-person decision making (MPDM) that uses hesitant fuzzy preference relations (HFPRís) that arenít in the usual format. We proposed a Lukasiewicz transitivity (TL-transitivity)-based technique for establishing normalised hesitant fuzzy preference relations (NHFPRís) at the most essential level, after that, a model based on consensus is constructed. After that, a transitive closure formula is created in order to build TL -consistent hesitant fuzzy preference relations (HFPRís) and symmetrical matrices. Afterwards, a consistency analysis is performed to determine the degree of consistency of the data given by the decision makers (DMs), as a result, the consistency weights must be assigned to them. After combining consistency weights and preset(predeÖned) priority weights, the Önal priority weights vector of DMs is obtained (if there are any). The consensus process determines either data analysis and selection of a suitable alternative should be done directly or externally. The enhancement process aims to improve the DMís consensus measure, despite the implementation of an indicator for locating sluggish points, in the circumstance that an unfavorable agreement is achieved. Finally, a comparison case demonstrates the relevance and e§ectiveness of the proposed system. The conclusions indicate that the suggested strategy can provide insight into the MPDM system.
模糊犹豫信息下群体决策的共识模型
本文提出了一种改进的基于共识的方法来处理多人决策(MPDM),该方法使用了通常格式为arenít的犹豫模糊偏好关系(HFPRís)。我们提出了一种基于Lukasiewicz传递性(TL-transitivity)的技术,在最本质的层面上建立规范化的犹豫模糊偏好关系(NHFPRís),然后构建了一个基于共识的模型。然后,建立传递闭包公式,构建TL一致的犹豫模糊偏好关系(HFPRís)和对称矩阵。然后,通过一致性分析来确定决策者给出的数据的一致性程度,从而为决策者分配一致性权重。将一致性权值与预置的优先级权值(predeÖned)结合,得到dm的优先级权值向量Önal(如果有的话)。共识过程决定了数据分析和选择合适的替代方案应该直接或外部进行。此次强化过程的目的是,在达成不利协议的情况下,虽然实施了“滞后点定位指标”,但仍要完善“DMís共识措施”。最后,通过一个比较案例验证了所提系统的相关性和有效性。研究结果表明,本文提出的策略有助于深入了解MPDM系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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