在使用修改后的TOPSIS与R-norm-q-rung图片模糊信息测量绿色供应商选择的过程中
Himanshu Dhumras1, Rakesh K Bajaj1, Varun Shukla2
1Department of Mathematics, Jaypee University of Information Technology, Waknaghat, Solan, 173234 HP India.
概括
一种新的R-norm q-rung图片模糊区分信息测量方法为不准确的信息提供了更大的灵活性. 这一通用措施可以在复杂的场景中改善决策,例如绿色供应商选择.
科学领域:
- 决策科学 决策科学 决策科学
- 模糊的集合理论 模糊的集合理论
- 信息化措施 信息化措施
背景情况:
- 在决策过程中,处理不准确的信息至关重要.
- 现有的模糊集合理论在灵活性上有局限性.
- 图像模糊集和q-rung orthopair模糊集提供部分解决方案.
研究的目的:
- 引入一个一般化的R-norm q-rung图像模糊的区分信息测量.
- 提高信息表示的灵活性.
- 将措施应用于绿色供应商选择问题.
主要方法:
- 开发了一个新的参数歧视性信息措施.
- 使用了q-rung图片模糊集 (q-RPFS) 框架.
- 将该措施整合到类似于理想解决方案的订单偏好技术 (TOPSIS) 方法中.
主要成果:
- 拟议的措施有效地处理不准确的信息,具有更大的灵活性.
- 对绿色供应商选择的应用证明了该模型的一致性.
- 该方法为不确定性下决策提供了一个强大的方法.
结论:
- R-norm q-rung图像模糊区分信息测量是复杂决策问题的强大工具.
- 在现有的模糊模型中,q-RPFS框架提供了显著的优势.
- 该研究验证了拟议的方法在绿色供应链管理中的实际应用.
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