| 引用本文: | 吴启迪,马玉敏,李莉,乔非.数据驱动下的半导体生产线动态调度方法[J].控制理论与应用,2015,32(9):1233~1239.[点击复制] |
| WU Qi-di,MA Yu-min,LI Li,QIAO Fei.Data-driven dynamic scheduling method for semiconductor production line[J].Control Theory and Technology,2015,32(9):1233~1239.[点击复制] |
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| 数据驱动下的半导体生产线动态调度方法 |
| Data-driven dynamic scheduling method for semiconductor production line |
| 摘要点击 4090 全文点击 1398 投稿时间：2015-03-26 修订日期：2015-07-27 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2015.50231 |
| 2015,32(9):1233-1239 |
| 中文关键词 半导体生产线 动态调度 数据驱动 特征选择 分类算法 |
| 英文关键词 semiconductor production line dynamic scheduling data-driven feature selection classification method |
| 基金项目 国家自然科学基金项目(610340004, 61273046, 51475334), 中央高校基本科研业务费专项资金资助. |
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| 中文摘要 |
| 本文研究了一种数据驱动下的半导体生产线调度框架, 该框架基于调度优化数据样本, 应用机器学习算法,获得动态调度模型, 通过该模型, 对于半导体生产线, 能够根据其当前的生产状态, 实时地定出近似最优的调度策
略. 在此基础上, 利用特征选择和分类算法, 提出一种生成动态调度模型的方法, 并且具体实现出一种混合式特征选择和分类算法的调度模型: 先采用过滤式特征选择方法对生产属性进行初步筛选, 然后再采用封装式特征选择和分类方法生成模型以提高模型生成的效率. 最后, 在某实际半导体生产线上, 对在所提出的框架上采用6种不同算法实现的动态调度模型进行测试, 并对算法性能数据和生产线性能据进行对比和分析. 结果表明, 数据驱动下的动态调度方法优于单一的调度规则, 同时也能满足生产线调度实时性要求. 在数据样本较多的情况下, 建议采用本文所提出的方法. |
| 英文摘要 |
| This paper investigates a framework of data-driven scheduling method for semiconductor production line.
Based on optimized scheduling data samples, this framework applies machine learning algorithm to obtain the dynamic
scheduling model. Using this model, we determine the approximate optimal scheduling strategy for the production line
under the current production status. On this basis, we propose a method based on feature selection and classification to
generate a dynamic scheduling model, and provide a realization by means of a hybrid algorithm of feature selection and
classification as such: production attributes are primarily selected by using a filter selection algorithm, and then the wrapped
selection and classification algorithm is employed to produce the scheduling model. On a real semiconductor production
line, the proposed framework is tested by using the dynamic scheduling models realized by 6 different algorithms. The
performance data of algorithms and the production performance using different scheduling methods are compared and ana-
lyzed. The data shows that the data-driven dynamic scheduling methods are superior to the method with simple dispatching
rule, and satisfy the real-time scheduling requirements for the production line. In the case with large size of sample data,
the application of the proposed method is even preferable. |
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