图片模糊的添加比率评估方法 (ARAS) 和VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) 方法用于多属性决策问题及其应用
Jianping Fan1, Dongshuai Han1, Meiqin Wu1
1School of Economics and Management, Shanxi University, Taiyuan, 030006 China.
Complex & intelligent systems
|June 26, 2023
概括
本研究引入了一种用于模糊图像环境的新型多属性决策方法,通过图像模糊数字 (PFN) 增强绿色供应商选择. 该方法有效地确定属性权重,并扩展现有的决策技术.
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:
- 多属性决策 (MADM) 问题是复杂的,特别是在不确定性下.
- 模糊集合理论及其扩展,如图片模糊集合 (PFS),提供了处理不准确信息的框架.
- 现有的MADM方法需要适应,以有效地利用图片模糊数字 (PFN) 提供的丰富信息.
研究的目的:
- 为模糊的图像环境开发一种新的多属性决策 (MADM) 方法.
- 引入一种新的方法来比较图片模糊数 (PFN).
- 将ARAS和VIKOR方法扩展到图像模糊环境中,以改善决策支持.
主要方法:
- 一种用于图片模糊数 (PFN) 的新比较方法.
- 在图像模糊环境中应用相关系数和标准偏差 (CCSD) 方法来确定属性权重,即使有未知的权重.
- 将ARAS和VIKOR方法扩展到图像模糊环境 (PFS-ARAS和PFS-VIKOR),使用拟议的PFN比较规则.
主要成果:
- 提出的方法有效地解决图片模糊环境中的多属性决策问题.
- 开发的方法已成功应用于绿色供应商选择问题.
- 相对应系数和标准偏差 (CCSD) 方法在部分或完全无知的情况下证明对属性权重的确定具有稳定性.
结论:
- 引入的PFN比较规则和扩展的决策方法为模糊图像信息的MADM问题提供了强大的框架.
- 该方法为复杂的决策场景提供了宝贵的工具,特别是在绿色供应商选择方面.
- 这项研究表明,图片模糊数字在提高决策准确性和处理模两可方面具有实用性.
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