开发基于Aczel-Alsina t-规范的线性二氧化模糊聚合运算符及其在多标准决策中的应用,未知权重信息
Rizwan Gul1, Tareq M Al-Shami2,3,4, Saba Ayub1
1Department of Mathematics, Quaid-i-Azam University, Islamabad, 44230, Pakistan.
Heliyon
|September 9, 2024
概括
本研究介绍了线性二奥芬丁模糊集合 (LD-FSs) 的Aczel-Alsina操作规则,增强决策模型. 新的Aczel-Alsina聚合运营商在多标准决策 (MCDM) 问题中改善了对模两可的数据的处理.
科学领域:
- 模糊的集合理论和决策科学
- 数学优化和运营研究 数学优化和运营研究
背景情况:
- 与其他家族相比,Aczel-Alsina t-规范和t-规范在聚合中提供了更高的灵活性和稳定性.
- 线性二氧化碳模糊集 (LD-FSs) 是模糊集的高级扩展,擅长在决策分析中管理不确定和模两可的数据.
- 现有的模糊集扩展,如FS,IFS,PyFS和q-ROFS在处理复杂数据模糊性方面存在局限性.
研究的目的:
- 建立Aczel-Alsina运算规则 (和,乘法,标量乘法,指数式) 专门用于线性二奥法模糊集 (LD-FSs).
- 介绍和分析线性二氧化碳模糊阿尔-阿尔西纳加权平均 (LDFAAWA) 和线性二氧化碳模糊阿尔-阿尔西纳加权几何 (LDFAAWG) 运算符.
- 在LD-FS框架内开发和验证使用这些新型聚合运算符的多标准决策 (MCDM) 方法.
主要方法:
- 对LD-FSs的Aczel-Alsina和,乘,标量乘和指数的定义.
- 基于定义的Aczel-Alsina操作的LDFAAWA和LDFAAWG聚合运营商的制定.
- 开发的聚合运算符的应用在LD-FS条件下的天气预报中对实际的MCDM案例研究.
主要成果:
- 拟议的Aczel-Alsina操作规则和聚合运营商 (LDFAAWA,LDFAAWG) 在LD-FS框架内有效处理模糊数据.
- 与案例研究中的现有方法相比,开发的MCDM模型显示出更高的准确性和性能.
- 分析并突出了参数 Λ 对替代方案排名顺序的影响,从而提供了对模型灵敏度的见解.
结论:
- 阿切尔-阿尔西纳与LD-FS的整合为复杂的决策场景提供了强大而通用的工具.
- 拟议的LDFAAWA和LDFAAWG运营商为涉及不确定的信息的MCDM问题提供了更高的稳定性和准确性.
- 该研究通过实际应用验证了新方法的有效性,证实了其在现实世界中提供决策支持的潜力.
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