大数据环境下MES作业计划与调度能力云服务化研究

云制造技术给制造企业带来机遇的同时,也为其制造执行系统MES的设计与实现带来了新的挑战。为了解决单件小批MES中作业计划与调度优化问题,首先设计了一个从作业计划静态制定,到作业执行情况实时监控与主动感知,再到异常事件智能响应,最后到作业调度动态调节的闭环体系结构。接着针对异常信息实时获取与异常事件发现、异常事件智能化处理以及作业计划与调度优化算法计算能力服务化三个子问题,依次进行了问题分析并给出了技术解决方案。最后,以哈尔滨电机厂为案例对象,综合利用IEC/ISO 62264标准、大数据分析与挖掘方法以及由虚拟化、服务化和SOA等组成的云计算技术实现了单件小批MES作业计划与调度综合优化系统,验证了上述理论与方法的有效性。

A cloud servitization method for job shop scheduling capability of MES in big data environment

Cloud manufacturing brings new opportunities for manufacturing enterprises, but in the meantime it also brings new challenges to the design and implementation of manufacturing execution system (MES). To solve the issues of "job shop scheduling" in single and small batch MES, firstly we design a closed loop architecture that is from static scheduling to real time monitoring and active perception of manufacturing execution, then to intelligent response of abnormal events, and finally, to dynamic scheduling. Then for solving three sub issues, i.e. real time acquisition of exception information and discovery of abnormal events, intelligent processing of abnormal events and servitization of job shop scheduling optimization algorithms, we analyze them and provide technical solutions. Finally, taking Harbin Electrical Machinery Plant as a case, and combining IEC/ISO 62264 standard, big data analysis and mining method, and the cloud computing method consisting of virtualization, servitization and SOA together, we develop an integrated job shop scheduling optimization system of single and small batch MES, and the aforementioned theory and method are validated.