Dynamic scheduling for complex manufacturing system based on extreme learning machine

被引：0
|
作者：
Ma Y. [1 ]
Lu X. [1 ]
Qiao F. [1 ]
Shen Y. [1 ]
机构：
[1] School of Electronics and Information Engineering, Tongji University, Shanghai
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2021年 / 27卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Complex manufacturing system; Data driven; Dynamic scheduling; Extreme learning machine; Fuzzy C means clustering;
D O I：
10.13196/j.cims.2021.04.012
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Department of CIMS. All right reserved.
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页码：1081 / 1088
页数：7
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