(2102-6461)在不确定环境下降低决策者决策效率的两种新方法

IF 1.9 4区 数学 Q1 MATHEMATICS
O. Dalkl
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引用次数: 1

摘要

与其他数学模型不同,软集理论提供了参数化工具。然而,在这个理论中,由于隶属度表示为0和1,对于(0;1),我们无法确定任何对象是否属于参数。研究人员试图通过确保决策者表达这些价值观来克服这种情况。然而,我们无法知道决策者提供给我们的数据的准确性。因此,在本研究中,我们引入了关系隶属函数、关系非隶属函数、逆关系隶属函数和逆关系非隶属函数的概念,并研究了这些概念的相关性质。然后,我们提出了两种新的方法,使不确定性能够以一种理想的方式表达,并可用于决策。最后,对给出的方法和文献中一些重要的方法进行了比较和分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
(2102-6461) Two novel approaches that reduce the effectiveness of the decision maker in decision making under uncertainty environments
Unlike other mathematical models, soft set theory provides a parameterization tool contribution. However, in this theory, since membership degrees are expressed as 0 and 1, for (0; 1), we cannot determine whether any object belongs to a parameter or not. Researchers have tried to overcome this situation by ensuring that the decision maker expresses these values. However, we cannot know the accuracy of the data provided to us by the decision maker. Therefore, inthis study, we introduced the concepts of relational membership function, relational non-membership function, inverse relational membership function and inverse relational non-membership function and examined the related properties of these concepts. Then, we propose two new approaches so that uncertainty can be expressed in an ideal way and canbe used in decision-making. Finally, the approaches given and some of the important approaches in the literature are compared and analyzed.
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来源期刊
CiteScore
3.50
自引率
16.70%
发文量
0
期刊介绍: The two-monthly Iranian Journal of Fuzzy Systems (IJFS) aims to provide an international forum for refereed original research works in the theory and applications of fuzzy sets and systems in the areas of foundations, pure mathematics, artificial intelligence, control, robotics, data analysis, data mining, decision making, finance and management, information systems, operations research, pattern recognition and image processing, soft computing and uncertainty modeling. Manuscripts submitted to the IJFS must be original unpublished work and should not be in consideration for publication elsewhere.
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