考虑订单交货期的柔性作业车间主动调度方法  

An Active Scheduling Method for Flexible Job Shops Considering Order Delivery Deadlines

作　　者：朱海华[1] 陶帅 王健杰 张毅 唐敦兵[1] 刘长春 ZHU Haihua;TAO Shuai;WANG Jianjie;ZHANG Yi;TANG Dunbing;LIU Changchun(College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京航空航天大学机电学院,江苏南京210016

出　　处：《工业工程》2025年第2期58-68,共11页Industrial Engineering Journal

基　　金：国家自然科学基金资助项目(92267109);江苏高校“青蓝工程”资助项目;江苏省卓越博士后计划资助项目(2024ZB194);中国博士后科学基金资助项目(2024M754122)。

摘　　要：随着物联网技术与工业基础设施的迅猛发展,制造现场数字化网络化能力不断攀升,为离散制造车间生产任务的智能调度与管控奠定了技术基础与保障。现阶段产品多品种、小批量、定制化的需求不断增加,导致生产现场的车间环境越来越复杂多变,订单交货期不确定性变大,而订单剩余完工时间则是影响交货期的关键因素之一。基于车间现场强大的数据感知与获取能力,提出一种考虑订单交货期的柔性作业车间主动调度方法。首先建立基于改进深度Q网络的调度决策模型,将订单剩余完工时间预测值作为决策模型的状态特征之一,增强调度模型的主动性;针对工件分派到机器以及机器缓冲区的工件选择问题设计复合调度规则动作集;然后,以最大完工时间、最大总延期时间、最大平均延期时间等为优化目标,根据实时数据通过预测网络和目标网络来训练决策模型选择最优动作,进而实现生产过程的主动调度,并保证多目标全局优化效果;最后,通过应用案例验证证明所提调度方法的有效性和优越性。With the rapid development of the Internet of Things technology and industrial infrastructure,the digitalization and networking of manufacturing sites are continuously improving,providing technical foundation and guarantee for intelligent scheduling and control of production tasks in discrete manufacturing job shops.At present,the growing demand for multi-variety,small-batch,and customized products is making the workshop environment increasingly complex and variable,thereby amplifying the uncertainty of order delivery deadlines.However,the remaining completion time of orders is a key factor affecting delivery deadlines.Based on the strong data perception and acquisition ability of shop sites,an active scheduling method for flexible job shops considering order delivery deadlines is proposed.Firstly,a scheduling decision model based on an improved deep Q-network is established,where the predicted remaining completion time of orders is incorporated as one of the state features of the decision-making model to enhance the proactivity of scheduling.A composite scheduling rule action set is designed for the problem of job assignment to machines and job selection in machine buffers.The optimization objectives are to minimize the maximum completion time,total delay time,and average delay time.The decision-making model is trained to select the optimal action according to the real-time data through the prediction network and the target network,so as to realize active scheduling of the production process while ensuring multi-objective global optimization.Finally,the effectiveness and superiority of the proposed scheduling method are verified by application.

关 键 词：订单交货期 柔性作业 主动调度 改进深度Q网络 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391[自动化与计算机技术—控制科学与工程]