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双重犹的固态模糊的哈马切尔聚合运营商和TOPSIS及其应用于多标准决策的应用
Muhammad Amman1, Tabasam Rashid1, Asif Ali1,2
1Department of Mathematics, School of Sciences, University of Management and Technology, Lahore, Pakistan.
PloS one
|October 24, 2024
概括
这项研究引入了双犹的费尔马特模糊集 (DHFFS),提高了决策灵活性. 新的哈马切尔运营商在复杂的多重标准决策 (MCDM) 问题中提高了准确性和效率.
科学领域:
- 模糊的集合理论 模糊的集合理论
- 决策科学 决策科学
- 计算智能是一种计算智能.
背景情况:
- 现有的模糊集合理论在处理具有固有不确定性的复杂决策场景方面存在局限性.
- 费尔马特模糊集和双犹集提供部分解决方案,但缺乏用于现实世界的应用的集成灵活性.
- 需要先进的数学结构来提高多重标准决策 (MCDM) 方法的精度和适应性.
研究的目的:
- 引入和定义双犹的费尔马特模糊集合 (DHFFS) 作为一种新的不确定性表示框架.
- 根据哈马赫的T-norm和T-conorm开发新的聚合运营商,用于DHFFS.
- 证明拟议的DHFFS及其相关运营商在解决MCDM问题的有效性.
主要方法:
- 双犹的费尔马特模糊集 (DHFFS) 的定义,集成会员和非会员犹.
- 使用一般化的T-norm (T) 和T-conorm (T*) 运算,推导出改进的结合和交叉公式.
- 新型聚合运算符的开发:Hamacher加权算术,几何学,功率算术和功率几何学用于DHFFS.
- 应用案例研究来验证在现实世界的决策环境中提出的方法和操作人员.
- 进行比较和敏感性分析,以评估与传统方法相比的性能.
主要成果:
- DHFFS框架为决策提供了更强的适应性和灵活性.
- 与传统技术相比,提出的基于哈马切尔的聚合运营商表现出更高的效率和准确性.
- 案例研究结果证实了DHFFS方法在大规模决策过程中的实际适用性和有效性.
结论:
- 双犹的费尔马特模糊集 (DHFFS) 为处理决策中的复杂不确定性提供了强大而灵活的结构.
- 开发的Hamacher聚合运算器显著提高了MCDM方法的性能.
- 拟议的方法对现实世界决策场景具有强烈的实际意义,提高了解决方案的可靠性.
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