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Neural networks in production scheduling: Intelligent solutions and future promises

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dc.contributor.author Metaxiotis, K en
dc.contributor.author Psarras, J en
dc.date.accessioned 2014-03-01T01:19:19Z
dc.date.available 2014-03-01T01:19:19Z
dc.date.issued 2003 en
dc.identifier.issn 0883-9514 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/15418
dc.subject Production Scheduling en
dc.subject Neural Network en
dc.subject.classification Computer Science, Artificial Intelligence en
dc.subject.classification Engineering, Electrical & Electronic en
dc.subject.other Computer simulation en
dc.subject.other Decision making en
dc.subject.other Enterprise resource planning en
dc.subject.other Marketing en
dc.subject.other Planning en
dc.subject.other Scheduling en
dc.subject.other Technological forecasting en
dc.subject.other Hybrid artificial neural networks en
dc.subject.other Stand-alone artificial neural networks en
dc.subject.other Neural networks en
dc.title Neural networks in production scheduling: Intelligent solutions and future promises en
heal.type journalArticle en
heal.identifier.primary 10.1080/713827140 en
heal.identifier.secondary http://dx.doi.org/10.1080/713827140 en
heal.language English en
heal.publicationDate 2003 en
heal.abstract In recent times, Artificial Neural Networks (ANNs) have been receiving increased attention as tools for business applications. In this paper, we focus on the use of ANNs in the field of production scheduling. The history of ANNs in production scheduling is outlined, leading to a discussion of the various approaches, as well as the current research directions. The paper concludes by sharing thoughts and estimations on the future prospects of ANNs in this area. en
heal.publisher TAYLOR & FRANCIS INC en
heal.journalName Applied Artificial Intelligence en
dc.identifier.doi 10.1080/713827140 en
dc.identifier.isi ISI:000182024500004 en
dc.identifier.volume 17 en
dc.identifier.issue 4 en
dc.identifier.spage 361 en
dc.identifier.epage 373 en


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