一种新的方法,在EDAS方法中使用犹不决的直觉模糊的语言爱因斯坦聚合运算符,用于多标准组决策
Shahzad Faizi1, Wojciech Sałabun2, Mubashar Shah1
1Department of Mathematics, Virtual University of Pakistan, Pakistan.
Heliyon
|December 13, 2024
概括
基于平均解决方案距离的评估 (EDAS) 方法增强了对犹不决的直观模糊语言术语集的多标准组决策. 它提供了一个强大的方法,在不确定性中选择最佳选择.
科学领域:
- 决策科学 决策科学
- 模糊的逻辑 模糊的逻辑
- 运营研究 运营研究
背景情况:
- 多标准决策 (MCDM) 往往涉及不确定性和模糊性.
- 犹的直觉模糊的语言术语集 (HIFLTSs) 提供了一个框架来建模复杂的决策偏好.
- 现有的方法可能无法完全捕捉语言不确定性的群体决策的细微差别.
研究的目的:
- 为HIFLTSs适应基于距离平均解决方案 (EDAS) 的评估技术.
- 为HIFLTSs.引入新的运营法律和聚合运营商.
- 解决不确定的语言信息带来的多标准组决策 (MCGDM) 问题.
主要方法:
- 对HIFLTS的运营法律和聚合运营商的定义.
- 介绍犹的直觉模糊的语言爱因斯坦加权平均 (HIFLEWA) 运算符.
- 应用EDAS技术使用MCGDM的拟议运营商.
主要成果:
- 开发了一种基于EDAS的MCGDM与HIFLTS的新方法.
- 该方法有效地使用语言术语处理决策者的不确定性.
- 一个数值示例证明了该方法的有效性和逻辑健全性.
结论:
- 拟议的EDAS方法为复杂的决策场景提供了可靠的工具.
- 它为现有的MCDM技术提供了有价值的替代方案,特别是在语言不确定性方面.
- 该研究有助于在模糊环境中推进决策分析.
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