一个基于复杂的n,m-rung orthopair fuzzy aczel-alsina运算符的新型聚合框架,用于可再生能源决策
1Department of Mathematics, College of Sciences, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia. iealshamri@uhb.edu.sa.
Scientific reports
|November 1, 2025
概括
本研究引入了一个新的决策框架,使用复杂的n,m-rung orthopair fuzzy (Cn,m-ROF) 集和aczel-alsina操作来进行多属性决策 (MADM). 在一项可再生能源选择案例研究中,风能被发现是最佳选择.
科学领域:
- 决策科学 决策科学
- 模糊的数学 模糊的数学
- 运营研究 运营研究
背景情况:
- 在复杂的决策场景中,管理不确定性和模糊性至关重要.
- 现有的多重属性决策 (MADM) 方法经常与不准确或模糊的信息作斗争.
研究的目的:
- 开发一个先进的决策框架,使用复杂的n,m-rung orthopair fuzzy (Cn,m-ROF) 集和aczel-alsina聚合运算.
- 为了引入和分析新的聚合运营商:Cn,m-ROFAAWA和Cn,m-ROFAAWG.
- 通过一个可再生能源选择案例研究来证明框架的有效性.
主要方法:
- 开发两个新的聚合运营商:Cn,m-ROFAAWA (加权平均) 和Cn,m-ROFAAWG (加权几何).
- 运算符的理论性质的制定和检查,包括边界性,idempotency和单调性.
- 该框架应用于可再生能源选择问题进行验证.
主要成果:
- 在可再生能源选择案例研究中,风能在各种参数设置中始终排名最高.
- 与现有方法相比,拟议的Cn,m-ROF运营商在区分替代品和提高决策精度方面表现出卓越的表现.
- 分析证实了框架的可靠性,稳定性和实际适用性,较小的参数值改善了差异化.
结论:
- 综合复杂的n,m-rung整形夫妇模糊性与aczel-alsina手术的整合为MADM提供了一种有效的方法.
- 开发的框架为管理决策中的不确定性和模糊性提供了一个强大的工具.
- 未来的研究可以将这种方法扩展到其他模糊信息框架,以获得更广泛的应用.
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