Abstract:

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.

Key words: cloud manufacturing;job shop scheduling;big data;virtualization