有效的多属性组决策方法,以研究天文学在概率的语言q-rung orthopair fuzzy VIKOR框架
Sumera Naz1, Areej Fatima1, Shariq Aziz But2
1Department of Mathematics, Division of Science and Technology, University of Education, Lahore, Pakistan.
Heliyon
|July 18, 2024
概括
这项研究引入了一个新的模糊逻辑框架,使用概率语言 q-rung orthopair 模糊集 (PLq-ROFS) 来改善天文决策. 新的PLq-ROFS-VIKOR模型成功地确定了宇宙学作为一个案例研究中的最佳发现.
科学领域:
- 天文学和天体物理学.
- 决策科学 决策科学
- 模糊逻辑应用程序 模糊逻辑应用程序
背景情况:
- 多属性组决策 (MAGDM) 在天文学中是复杂的,因为数据不确定.
- 现有的模糊集合难以同时处理天文数据中的随机和非随机不确定性.
- 概率语言 q-rung orthopair 模糊集 (PLq-ROFS) 为不确定性管理提供了一个强大的框架.
研究的目的:
- 为天文学中MAGDM开发一种基于模糊逻辑的新框架.
- 为概率语言信息引入新的聚合运算符.
- 将PLq-ROFS与VIKOR模型集成,以加强决策支持.
主要方法:
- 利用概率语言 q-rung orthopair 模糊集 (PLq-ROFS) 来建模不确定的天文数据.
- 拟议的新型聚合运营商:PLq-ROF权重功率平均 (PLq-ROFWPA) 和PLq-ROF权重功率几何 (PLq-ROFWPG).
- 将PLq-ROFS框架与VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) 模型集成,从而创建了PLq-ROF-VIKOR模型.
主要成果:
- 拟议的PLq-ROF-VIKOR模型有效地对复杂的天文决策问题的替代解决方案进行排名.
- 参数和比较分析证明了模型的效率和准确性.
- 一个天文学现实世界案例研究证实了该模型的实际适用性.
结论:
- PLq-ROF-VIKOR模型为处理天文MAGDM中的不确定性提供了一种优越的方法.
- 宇宙学被确定为天文学案例研究中最优的关键发现.
- 该框架在天文数据分析的现有模糊集方法上提供了显著的优势.
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