一种语言犹不决的模糊群组决策方法,用于可持续的人机协作
Xuejiao Zhang1, Yu Yang1, Qian Chen2,3
1College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China.
PloS one
|October 9, 2025
概括
这项研究引入了一种新的人机匹配方法,用于智能制造,提高生产效率和环境可持续性. 它针对复杂产品组装的人机协作单元 (HRCU) 的个人和团体行为.
科学领域:
- 智能制造是一个智能制造.
- 人与机器人的协作
- 决策科学 决策科学 决策科学
背景情况:
- 人机协作单元 (HRCU) 的配置和分配对于复杂制造中的生产性能至关重要.
- 现有的研究在HRCU构建中不足以解决个人非理性和群体参考行为的问题.
- 犹不决的模糊环境在人机器人匹配的决策中带来了挑战.
研究的目的:
- 为HRCU提出一个稳定的一对多人机器人位置匹配决策 (HRPMDM) 方法.
- 为了考虑到语言术语选择和会员学位分配中的双重不确定性.
- 将碳中和,成本优化和服务质量 (QoS) 整合到HRCU分配战略中.
主要方法:
- 使用语言犹模糊集 (LHFSs) 来描述评估者不确定性.
- 使用云模型用于LHFS的定量转换和基于认知相似性的聚类.
- 开发了一个基于社交网络的算法,用于精确的子组和成员权重量化,集成到组共识调整中.
- 引入了不确定匹配偏好排名 (UMPRs) 的多命题信念结构和满意度测量方法.
主要成果:
- 拟议的HRPMDM方法有效地根据认知相似性将匹配对象划分为子组.
- 社交网络分析减少了属性权重评估中的偏差,提供了可靠的参考校正值.
- 该方法成功地将碳中和性成本优化和QoS集成到HRCU分配中.
- 一个案例研究验证了拟议方法的操作可行性和性能有效性.
结论:
- 这种新的HRPMDM方法提高了HRCU在犹不决的模糊环境中的决策能力.
- 该方法有效地管理个人和团体的行为,提高匹配的准确性和可靠性.
- 在复杂产品制造方面,生产效率和环境可持续性得到了显著改善.
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