关键词: 动态调度, 数据驱动, 极限学习机, 模糊C均值聚类, 复杂制造系统

Abstract: To improve the effectiveness of the dynamic scheduling for complex manufacturing systems,a data driven dynamic scheduling method was proposed.In the proposed method,the composite rule set was embedded in the scheduling sample data,and then the scheduling sample data were optimized through the Design of Experiment (DOE).To improve the accuracy and efficiency of the dynamic scheduling,the Fuzzy C Means (FCM) clustering and the Extreme Learning Machine (ELM) approaches were successively used to cluster and learn scheduling models from the optimal sample set.As a result,the learnt model was applied to the dynamic scheduling.The proposed method was verified on a semiconductor manufacturing benchmark model,namely theMIMAC6 model.The results showed that it had greater improvements in both the long-term and short-term performance indicators of the manufacturing system compared with the single-rule scheduling approaches.Therefore,the system performance could be comprehensively optimized.

Key words: dynamic scheduling, data driven, extreme learning machine, fuzzy C means clustering, complex manufacturing system