基于犹豫模糊β邻域的改进TOPSIS多准则决策方法

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chenxia Jin, Jusheng Mi, Fachao Li, Meishe Liang
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引用次数: 3

摘要

多准则决策(MCDM)在许多应用领域中起着至关重要的作用。如果可用信息是清晰的,有许多经典的方法来解决MCDM问题。然而,MCDM固有的不确定性和模糊性往往使这些方法不适合解决这类问题。针对TOPSIS方法在犹豫不决模糊β覆盖近似空间(HFβCAS)中不能对备选方案进行完全排序的缺点,提出了一种改进的TOPSIS方法。首先,基于犹豫模糊β邻域定义了两对犹豫模糊关系,构造了相应的犹豫模糊覆盖粗糙集模型;我们进一步讨论了模型之间的性质和关系。其次,我们引入了一种新的综合权重确定方法,该方法利用了犹豫模糊覆盖粗糙集的精度度和最大偏差法。第三,我们构建了一个γ-βCHF-TOPSIS方法,将TOPSIS方法推广到HFβCAS中。最后,以两个实际决策问题为例,说明了γ-βCHF-TOPSIS方法的具体实施过程,验证了其有效性和合理性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An improved TOPSIS method for multi-criteria decision making based on hesitant fuzzy β neighborhood

An improved TOPSIS method for multi-criteria decision making based on hesitant fuzzy β neighborhood

Multi-criteria Decision Making (MCDM) plays a very vital role in many application fields. There are many classical methods to solve the MCDM problems if the available information is crisp. However, the uncertainty and ambiguity inherent in the MCDM often makes these methods unsuitable for solving this kind of problem. Aims at the failures of TOPSIS method that can not rank the alternatives completely in a Hesitant Fuzzy β-Covering Approximation Space (HFβCAS), we develop an improved TOPSIS method. First, we define two pairs of hesitant fuzzy relationship based on hesitant fuzzy β-neighborhood, and construct the corresponding hesitant fuzzy covering rough set models; further we discuss the properties and relationships between the models. Second, we introduce a new comprehensive weight determination method by using the precision degree of hesitant fuzzy covering rough set and the maximizing deviation method. Third, we construct a γ-βCHF-TOPSIS method to MCDM which generalizes the TOPSIS method in an HFβCAS. Finally, two real decision-making problems are used to illustrate the concrete implementation process of γ-βCHF-TOPSIS method, and demonstrate its effectiveness and reasonability.

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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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