A neural network for dispatching rule selection in a job shop

被引:3
作者
Ahmed El-Bouri
Pramit Shah
机构
[1] Ryerson University,Department of Mechanical and Industrial Engineering
来源
The International Journal of Advanced Manufacturing Technology | 2006年 / 31卷
关键词
Job shop; Neural networks; Dispatching rules; Makespan; Mean flowtime;
D O I
暂无
中图分类号
学科分类号
摘要
This paper investigates an intelligent system that selects dispatching rules to apply locally for each machine in a job shop. Randomly generated problems are scheduled using optimal permutations of three different dispatching rules on five machines. A neural network is then trained to associate between a statistical characterization of the job mix in each of these problems, with the best combination of dispatching rules to use. Once trained, the neural network is able to recommend for new problems a dispatching rule to use on each machine. Two networks are trained separately for minimizing makespan and the mean flowtime in the job shop. Test results show that the combinations of dispatching rules suggested by the trained networks produce better results, for both objectives, than the alternative of using a single rule common to all machines.
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收藏
页码:342 / 349
页数:7
相关论文
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