经编车间过程监控与生产调度

Process Monitoring and Production Scheduling for Warp Knitting Workshop

为解决经编车间监控手段落后而导致车间管理人员难以实时掌握车间生产情况的问题,在分析经编车间生产过程特点的基础上,设计开发了基于信息物理系统(cyber-physical system,CPS)的车间两级监控与生产调度系统。建立经编机信息模型对应物理实体,并赋予数据深刻内涵。采用BP神经网络构建了经编布织造时间预测模型,用以预估生产交期。提出一种改进遗传算法,利用监控系统的数据求解生产订单排程,辅助解决经编车间生产决策问题。应用该系统后,员工生产积极性与设备利用率均有所提高,且操作工人数减少10%,产能提高2%。

Due to the backward monitoring mechanism of the warp knitting workshop,the administrator could not grasp the production process of the workshop in time.To solve this problem,the two-stage monitoring and production scheduling system based on cyber-physical system(CPS)was designed and developed through a further analysis of the characteristics of the production process in the warp knitting workshop.A warp knitting machine information model which gave the data a deep connotation was established to correspond to physical entity.A prediction model of warp knitting time was constructed to estimate production delivery based on BP neural network.An improved genetic algorithm was proposed to solve the production order scheduling according to the data obtained from the monitoring system.It can effectively solve the production decision problem in warp knitting workshop.After applying this system,the employee motivation for production and equipment utilization rate have been improved.The number of operating workers reduces by 10%,and production capacity increases by 2%.