期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 17, 期 5, 页码 621-639出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2012.2227326
关键词
Dispatching rule; genetic programming; hyper-heuristic; job shop scheduling (JSP)
资金
- New Zealand Government [VUW0806, 12-VUW-134]
- Royal Society of New Zealand, and the University Research Fund [200457/3230]
- Victoria University of Wellington
Designing effective dispatching rules is an important factor for many manufacturing systems. However, this time-consuming process has been performed manually for a very long time. Recently, some machine learning approaches have been proposed to support this task. In this paper, we investigate the use of genetic programming for automatically discovering new dispatching rules for the single objective job shop scheduling problem (JSP). Different representations of the dispatching rules in the literature are newly proposed in this paper and are compared and analysed. Experimental results show that the representation that integrates system and machine attributes can improve the quality of the evolved rules. Analysis of the evolved rules also provides useful knowledge about how these rules can effectively solve JSP.
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