一个直觉模糊图的变化系数测量与应用选择一个可靠的联盟合作伙伴
Naveen Kumar Akula1, Sharief Basha S2, Nainaru Tarakaramu3
1Department of Mathematics, Mother Theresa Institute of Engineering and Technology, Palamaner, A.P, 517408, India.
Scientific reports
|August 3, 2024
概括
这项研究为直觉模糊组决策 (GDM) 问题引入了一种新的统计测量方法. 这种新的方法成功地将阿尔法确定为顶级联盟伙伴,证明了其在选择最佳解决方案方面的有效性.
科学领域:
- 运营研究 运营研究
- 管理科学 管理科学
- 图形理论 图形理论
- 模糊的数学 模糊的数学
背景情况:
- 集团决策 (GDM) 在多个学科中至关重要.
- 直觉模糊偏好关系 (IFPR) 在GDM中用于专家偏好表示.
- 在GDM中处理模两可或不可靠的数据需要强大的统计措施.
研究的目的:
- 为直觉模糊组决策 (GDM) 问题提出一种新的统计测量方法.
- 解决决策场景中含糊或不可靠数据所带来的挑战.
- 开发一种方法来选择最理想的替代品,特别是联盟伙伴.
主要方法:
- 用直觉模糊图 (IFG) 和拉普拉斯能量 (LE) 来测量变化系数的整合.
- 拉普拉斯能量用于确定个别标准重量,随后估计整体标准重量向量.
- 变化系数的测量用于确定每个标准的权威标准权重和排名替代品.
主要成果:
- 提出的技术在一个现实的GDM场景中成功实施,涉及四家公司 (Alpha,Beta,Delta,Zeta).
- 阿尔法被确定为理想的联盟伙伴,在评估的公司中排名第一.
- 该研究验证了新统计测量方法在选择最佳替代方案方面的有效性.
结论:
- 开发的统计指标为解决直觉模糊组决策问题的可靠方法提供了可靠的方法.
- 这种技术广泛适用于各种GDM场景,需要选择最佳解决方案.
- 这些发现强调了结合变化系数,IFG和LE的实用性,以进行可靠的决策分析.
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