犹不决的双模糊多属性信息融合与专家可靠性
Shshank Chaube1, Anuj Kumar2, Manoj Kumar Singh3
1Symbiosis Institute of Technology, Hyderabad Campus, Symbiosis International (Deemed University), Pune, India. shshank.chaube@sithyd.siu.edu.in.
Scientific reports
|April 24, 2025
概括
本研究引入了一种犹不决的双模糊信息融合 (HBFIF) 方法,通过整合专家可靠性 (ER) 来改善多属性决策 (MADM). 该方法有效地处理相互矛盾的专家意见,以获得更精确的结果.
科学领域:
- 决策科学 决策科学
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 多属性决策 (MADM) 需要专家的意见,但专家的可靠性和相互矛盾的意见可能会降低结果的准确性.
- 现有的方法很难完全整合专家的犹和冲突,同时保持原始数据的完整性.
- 专家可靠性 (ER) 对于在复杂场景中准确的决策结果至关重要.
研究的目的:
- 为MADM提出一种新的犹不决的双模糊信息融合 (HBFIF) 方法.
- 将专家可靠性 (ER) 纳入决策框架,特别是在处理相互矛盾的专家信息时.
- 加强专家偏好的聚合,同时保留原始数据和风险偏好.
主要方法:
- 开发了一个犹不决的双模糊信息融合 (HBFIF) 方法,结合了专家可靠性 (ER).
- 根据专家意见的相似性构建了一个可靠性指标.
- 使用功率平均 (PA) 操作员和TOPSIS启发的方法进行数据聚合和意见保存.
主要成果:
- 该HBFIF方法有效地将专家可靠性纳入决策过程.
- 这种方法成功地处理了犹和矛盾的专家信息.
- 通过选择用于汽车的储存方法来证明适用性和有效性.
结论:
- 拟议的HBFIF方法为MADM提供了一个强大的框架,具有不可靠和相互矛盾的专家数据.
- 整合专家可靠性可以提高决策结果的准确性和完整性.
- 该方法在现实世界的决策场景中具有实际的相关性和适用性,例如汽车技术选择.
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