教育机构选择的决策算法采用球形模糊的赫罗尼亚平均运算符和Aczel-Alsina三角规范
Abrar Hussain1, Kifayat Ullah1, Sajid Latif1
1Department of Mathematics, Riphah International University Lahore Campus, 54000, Lahore, Pakistan.
Heliyon
|April 11, 2024
概括
本研究介绍了球形模糊集 (SFS) 和Aczel Alsina操作,用于处理复杂数据的决策. 新的SFS Aczel Alsina Heronian意味着运营商可以加强对现实世界问题的信息聚合和分析.
科学领域:
- 模糊的集合理论 模糊的集合理论
- 决策分析 决策分析
- 信息聚合 信息聚合
背景情况:
- 球形模糊集 (SFSs) 提供了一个比直觉和图像模糊集更全面的框架.
- 决策者 (DM) 在聚合过程中面临复杂且信息不足的挑战.
- 阿齐尔·阿尔西纳三角规范因其在决策分析中的顺近似能力而闻名.
研究的目的:
- 在球形模糊 (SF) 信息环境中开发新的t-norm和t-conorm运算.
- 引入新的数学方法来分析SF数据,确保清晰度和信息充分性.
- 使用拟议的方法,建立一个对多属性决策 (MADM) 问题的算法.
主要方法:
- 球形模糊的发展 Aczel Alsina 赫伦平均值 (SFAAHrM) 和加权赫伦平均值 (SFAAWHrM) 运算符.
- 引入球形模糊的Aczel Alsina几何赫罗尼平均值 (SFAAGHrM) 和加权几何赫罗尼平均值 (SFAAWGHrM) 运算符.
- 属性的表征,以验证衍生数学方法的有效性.
主要成果:
- 小说SF Aczel Alsina 赫罗尼平均运算符和几何变量已成功开发.
- 提出的方法通过特征性属性来证明其有效性和有效性.
- 一个数值示例证实了用于提高教育机构绩效的衍生方法的兼容性.
结论:
- 该研究成功地将Aczel Alsina三角规范与球形模糊集集成在一起.
- 开发的运算符和MADM算法提供了一个强大的机制来处理复杂的决策场景.
- 这些发现为提高组织绩效提供了坚实的基础,特别是在教育环境中.
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