扰动累积下基于机器学习的重调度方式选择

Rescheduling Mode Selection under Recessive Disturbance Accumulation via Machine Learning

针对扰动累积下重调度方案甄选问题,提出了一种基于数据学习的重调度方式选择方法。利用累积误差时间来量化隐性扰动,用数据反映实时生产加工状况;构建扰动累积下重调度模型,基于遗传仿真获取评价重调度决策的因素、决策标签。分析仿真样本特征,并基于数据特征降维映射,构建基于支持向量机的分类决策模型以学习生产状况与重调度方式的内在联系,从而帮助生产管理者快速制定决策,提高响应速度。实验验证了所建立重调度方式选择框架的合理性和有效性。

A method of rescheduling mode selection was proposed to solve the problems of disturbance cumulative rescheduling scheme selection based on data learning.The cumulative disturbance time was used to quantify the implicit disturbance,and the real-time production and processing status were reflected by data.The cumulative disturbance rescheduling model was constructed,and the factors and decision labels of rescheduling decision were obtained based on genetic simulation.The characteristics of the simulation samples were analyzed,and based on the data feature reduction mapping,the SVM-based classification decision model was constructed to learn the internal relationship between the production situation and the rescheduling mode,which would help the production managers to make the decision quickly and improve the response speed.Finally,the rationality and effectiveness of the rescheduling selection framework were verified by experiments.