Dynamic scheduling approach based on SVM for semiconductor production line

被引：0

作者：

Ma, Yu-Min ^{[1
]}

Qiao, Fei ^{[1
]}

Chen, Xi ^{[1
]}

Tian, Kuo ^{[1
]}

Wu, Xing-Hao ^{[1
]}

机构：

[1] CIMS Research Center, Tongji University, Shanghai

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2015年 / 21卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Binary particle swarm optimization algorithm; Dynamic scheduling; Feature selection; Support vector machine;

D O I：

10.13196/j.cims.2015.03.018

中图分类号：

学科分类号：

摘要：

A dynamic dispatching strategy selection approach for semiconductor production line was researched for quick and reasonable choice of scheduling strategies. Based on historical data, the Support Vector Machine (SVM) was selected as a data mining tool to obtain SVM-based dynamic scheduling strategy classification model with for production line by optimizing production attributes subset with Binary Particle Swarm Optimization(BPSO) algorithm. Under any given production status, an approximate optimal scheduling strategy could be acquired through this model in real-time. In the evaluation of scheduling strategies, a multi-object evaluation method based on power functions and entropy weight method was employed to extend the approach's application range. The effectiveness and feasibility of proposed dynamic scheduling approach was tested in an actual semiconductor production line. ©, 2015, CIMS. All right reserved.

引用

页码：733 / 739

页数：6

共 16 条

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