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Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling
Yu HB(于海斌); Liang W(梁炜)
Department工业控制系统研究室
Source PublicationComputers & Industrial Engineering
ISSN0360-8352
2001
Volume39Issue:3-4Pages:337-356
Indexed BySCI ; EI
EI Accession number2001236533607
WOS IDWOS:000168736500009
Contribution Rank1
KeywordJob-shop Scheduling Neural Network Genetic Algorithm Gradient Search
AbstractThe expanded job-shop scheduling problem (EJSSP) is a practical production scheduling problem with processing constraints that are more restrictive and a scheduling objective that is more general than those of the standard job-shop scheduling problem (JSSP). A hybrid approach involving neural networks and genetic algorithm (GA) is presented to solve the problem in this paper. The GA is used for optimization of sequence and a neural network (NN) is used for optimization of operation start times with a fixed sequence. After detailed analysis of an expanded job shop, new types of neurons are defined to construct a constraint neural network (CNN). The neurons can represent processing restrictions and resolve constraint conflicts. CNN with a gradient search algorithm, gradient CNN in short, is applied to the optimization of operation start times with a fixed processing sequence. It is shown that CNN is a general framework representing scheduling problems and gradient CNN can work in parallel for optimization of operation start times of the expanded job shop. Combining gradient CNN with a GA for sequence optimization, a hybrid approach is put forward. The approach has been tested by a large number of simulation cases and practical applications. It has been shown that the hybrid approach is powerful for complex EJSSP.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS KeywordSEARCH STRATEGIES ; TUTORIAL SURVEY ; PART II ; CONSTRAINTS
WOS Research AreaComputer Science ; Engineering
Citation statistics
Cited Times:50[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/6908
Collection工业信息学研究室_工业控制系统研究室
Corresponding AuthorYu HB(于海斌)
AffiliationShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110015, China
Recommended Citation
GB/T 7714
Yu HB,Liang W. Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling[J]. Computers & Industrial Engineering,2001,39(3-4):337-356.
APA Yu HB,&Liang W.(2001).Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling.Computers & Industrial Engineering,39(3-4),337-356.
MLA Yu HB,et al."Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling".Computers & Industrial Engineering 39.3-4(2001):337-356.
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