一个多属性组决策模型,基于双双的语言派萨哥拉斯模糊多比聚合运算符,用于最佳选择潜在的全球供应商
Tmader Alballa1, Ahmed Alamer2, Khadija Nasir3
1Department of Mathematics, College of Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Heliyon
|August 16, 2024
概括
这项研究引入了新的多比聚合运算符,用于双双语言毕达哥拉斯模糊数 (2TLPFNs),以增强不确定性下的多属性组决策 (MAGDM). 为复杂的决策场景提出并验证了一种新型模型.
科学领域:
- 决策科学 决策科学
- 模糊的逻辑和不确定性的量化.
- 运营研究 运营研究
背景情况:
- 多属性组决策 (MAGDM) 对于复杂的选择至关重要,通常涉及不确定的信息.
- 毕达哥拉斯模糊集 (PFS) 扩展了直觉模糊集 (IFS),提供了强大的不确定性表示.
- 2-双语言皮塔哥拉斯模糊数字 (2TLPFN) 将语言术语与PFS集成,用于微妙的决策建模.
研究的目的:
- 开发新的多比聚合运算符,用于双双语言毕达哥拉斯模糊数 (2TLPFNs).
- 提出并验证一个MAGDM模型,在2TLPF环境中使用这些新运营商.
- 提高Dombi操作在决策中的灵活性和适用性.
主要方法:
- 构建双双语言皮塔哥拉斯模糊 (2TLPF) Dombi 聚合操作员: 2TLPFDWA, 2TLPFDOWA, 2TLPFDWG, 和 2TLPFDOWA.
- 对新开发的聚合运营商的独特特征和属性的分析.
- 开发一个MAGDM模型,利用这些运营商在2TLPF环境中的决策问题.
主要成果:
- 成功制定和分析了四个新的2TLPF Dombi聚合运算符.
- 通过实践案例研究证明了拟议的MAGDM模型的有效性.
- 验证了模型的实施,弹性和适用于处理不确定的集团决策的适用性.
结论:
- 开发的2TLPF Dombi聚合运营商为MAGDM提供了灵活有效的工具.
- 拟议的MAGDM模型提供了一个强大的框架,用于处理语言不确定性的复杂决策.
- 该研究通过整合PFS,语言术语和Dombi操作,有助于推进决策方法.
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