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dc.contributor.authorYildirim, Mehmet Bayram
dc.contributor.authorCakar, Tarik
dc.contributor.authorDoguc, Ufuk
dc.contributor.authorMeza, Jose L. Ceciliano
dc.date.accessioned2011-03-22T20:23:07Z
dc.date.available2011-03-22T20:23:07Z
dc.date.issued2006-05-11
dc.identifier.issn0360-8352
dc.identifier.urihttp://hdl.handle.net/10057/3443
dc.identifier.urihttp://dx.doi.org/10.1016/j.cie.2006.02.002
dc.descriptionThis is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. Accepted for publications to Computers and Industrial Engineering,Vol. 50, No. 1, May 2006 doi:10.1016/j.cie.2006.02.002
dc.description.abstractWhen there is a production system with excess capacity, i.e. more capacity than the demand for the foreseeable future, upper management might consider utilizing only a portion of the available capacity by decreasing the number of workers or halting production on some of the machines/production lines, etc. while preserving the flexibility of the production system to satisfy demand spikes. To achieve this flexibility, upper management might be willing to attain some pre-determined/desired performance values in a production system having identical parallel machines in each work center. In this study, we propose a framework that utilizes parallel neural networks to make decisions on the availability of resources, due date assignments for incoming orders, and dispatching rules for scheduling. This framework is applied to a flexible manufacturing system with work centers having parallel identical machines. The artificial neural networks were able to satisfactorily capture the underlying relationship between the design and control parameters of a manufacturing system and the resulting performance targets.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesComputers & Industrial Engineering 50 (2006) 185–194;
dc.subjectArtificial neural networksen_US
dc.subjectPriority rulesen_US
dc.subjectDue date assignmenten_US
dc.subjectFlexible manufacturing systemen_US
dc.subjectInverse schedulingen_US
dc.titleMachine number, priority rule, and due date determination in flexible manufacturing systems using artificial neural networksen_US
dc.typeArticleen_US


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