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dc.contributor.authorSha, DYen_US
dc.contributor.authorHsu, SYen_US
dc.date.accessioned2014-12-08T15:39:16Z-
dc.date.available2014-12-08T15:39:16Z-
dc.date.issued2004-05-01en_US
dc.identifier.issn0268-3768en_US
dc.identifier.urihttp://hdl.handle.net/11536/26821-
dc.description.abstractDue-date assignment (DDA) is the first important task of shop floor control in wafer fabrication. Due-date related performance is impacted by the quality of the DDA rules. Assigning order due dates and timely delivering the goods to the customer will enhance customer service and competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN) prediction is considered in this work. An ANN-based DDA rule combined with simulation technology and statistical analysis is developed. Besides, regression-based DDA rules for wafer fabrication are modelled as benchmarking. Whether neural networks can outperform conventional and regression-based DDA rules taken from the literature is examined. From the simulation and statistical results, ANN-based DDA rules perform a better job in due-date prediction. ANN-based DDA rules have a lower tardiness rate than the other rules. ANN-based DDA rules have better sensitivity and variance than the other rules. Therefore, if the wafer fab information is not difficult to obtain, the ANN-based DDA rule can perform better due-date prediction. The SFM_sep and JIQ in regression-based and conventional rules are better than the others.en_US
dc.language.isoen_USen_US
dc.subjectdue-date assignmenten_US
dc.subjectartificial neural networken_US
dc.subjectwafer fabricationen_US
dc.subjectsimulationen_US
dc.subjectshop floor controlen_US
dc.titleDue-date assignment in wafer fabrication using artificial neural networksen_US
dc.typeArticleen_US
dc.identifier.journalINTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGYen_US
dc.citation.volume23en_US
dc.citation.issue9-10en_US
dc.citation.spage768en_US
dc.citation.epage775en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000221468200019-
dc.citation.woscount26-
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