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	Learning single-machine robust scheduling heuristics subject to stochastic breakdowns using genetic programming
	Yin Wenjun ; Liu Min ; Wu Cheng
	2010-05-06 ; 2010-05-06
关键词	Theoretical or Mathematical/ genetic algorithms learning (artificial intelligence) robust control single machine scheduling/ single-machine robust scheduling genetic programming bi-tree structured individual job sequencing machine learning/ C1290F Systems theory applications in industry C1230L Learning in AI C1180 Optimisation techniques E1540 Systems theory applications E1010 Production management E0210G Optimisation
中文摘要	Stability is seldom considered in robust scheduling. This paper presents an analysis of the single-machine robust scheduling heuristics subject to stochastic breakdowns to minimize both the mean tardiness and the predictability. Idle times were inserted to absorb disruptions and a genetic programming (GP) system with bi-tree structured individuals was used to learn the effective heuristics. The results show that the evolved programs give good tardiness performance with good predictability. The programs integrate job sequencing with idle-time inserts and give satisfactory results even when applied to other environments. Hence the GP methods are good machine learning paradigms for robust scheduling problems in uncertain environments.
语种	英语 ; 英语
出版者	Tsinghua Univ. Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/8944]
专题	清华大学
推荐引用方式 GB/T 7714	Yin Wenjun,Liu Min,Wu Cheng. Learning single-machine robust scheduling heuristics subject to stochastic breakdowns using genetic programming[J],2010, 2010.
APA	Yin Wenjun,Liu Min,&Wu Cheng.(2010).Learning single-machine robust scheduling heuristics subject to stochastic breakdowns using genetic programming..
MLA	Yin Wenjun,et al."Learning single-machine robust scheduling heuristics subject to stochastic breakdowns using genetic programming".(2010).

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