基于图片立方体模糊聚合信息的COVID-19测试设施的多标准组决策
Muneeza1,2, Aiman Ihsan2, Saleem Abdullah1
1Department of Mathematics, Abdul Wali Khan University, Mardan, Pakistan.
Granular computing
|April 16, 2024
概括
这项研究为COVID-19测试设施的选择引入了新的图片立方体模糊几何聚合运算符. 这些新型运营商在不确定的环境中增强了多标准集团决策 (MCGDM).
科学领域:
- 模糊的数学 模糊的数学
- 决策科学 决策科学 决策科学
- 计算智能是一种计算智能.
背景情况:
- 信息聚合对于决策至关重要,特别是对于复杂的数据结构,如立方体和图像模糊数.
- 图像立方体模糊集提供了一个通用的框架来管理决策问题中增加的不确定性和模糊性.
研究的目的:
- 为图片立方体模糊信息引入新的哈马切尔几何聚合运算符.
- 开发和应用使用这些运营商选择COVID-19测试设施的多标准组决策 (MCGDM) 算法.
主要方法:
- 图像立方模糊的哈马切尔加权的几何,混合几何和顺序加权的几何聚合运算符的开发.
- 探索这些新定义的运算符的特性.
- 为图像立方体模糊环境量身定制的MCGDM算法的构建.
主要成果:
- 拟议的聚合运营商在处理图像立方体模糊信息方面表现出有效性.
- 开发的MCGDM算法成功选择了一个真实的实验室进行COVID-19测试.
- 对比分析证实了拟议运营商在现有方法上的优势.
结论:
- 新的图片立方模糊的哈马切尔几何聚合运算符对于MCGDM问题是有效的.
- 拟议的方法为在不确定性中选择COVID-19测试设施提供了一个强大的方法.
- 这项研究在现实世界决策场景中推进了模糊集合理论的应用.
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