Artificial neural network case study: The control of work-in-process inventory in a manufacturing line

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dc.contributor.authorCol Muhterem
dc.contributor.authorKarlik Bekir
dc.date.accessioned2024-07-22T08:25:54Z
dc.date.available2024-07-22T08:25:54Z
dc.date.issued1997
dc.description.abstractThe work-in-process in a middle-scale shoe factory is investigated. At the manufacturing center, there are five work departments composed of cutting, sewing, hand-leather working, assembling and quality control-packing. In these departments, there are work-in-process because of different processing time and insufficient number of labor-machines. Artificial neural network (ANN) with backpropagation is used for solving this work-in-process problem. The optimum number of labor-machines at work stations are obtained for work-in-process without hindering the production by considering processing times and flow at these work stations.
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/20666
dc.language.isoEnglish
dc.publisherIEEE
dc.subjectBackpropagation
dc.subjectIntelligent control
dc.subjectInventory control
dc.subjectNeural networks
dc.subjectProblem solving
dc.subjectQuality control
dc.subjectShoe manufacture
dc.subjectWork in process
dc.subjectProcess control
dc.titleArtificial neural network case study: The control of work-in-process inventory in a manufacturing line
dc.typeConference paper

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