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dc.contributor.authorMetan, G.en_US
dc.contributor.authorSabuncuoglu, I.en_US
dc.contributor.authorPierreval, H.en_US
dc.date.accessioned2016-02-08T09:55:24Z
dc.date.available2016-02-08T09:55:24Z
dc.date.issued2010en_US
dc.identifier.issn0020-7543
dc.identifier.urihttp://hdl.handle.net/11693/22089
dc.description.abstractA new scheduling system for selecting dispatching rules in real time is developed by combining the techniques of simulation, data mining, and statistical process control charts. The proposed scheduling system extracts knowledge from data coming from the manufacturing environment by constructing a decision tree, and selects a dispatching rule from the tree for each scheduling period. In addition, the system utilises the process control charts to monitor the performance of the decision tree and dynamically updates this decision tree whenever the manufacturing conditions change. This gives the proposed system the ability to adapt itself to changes in the manufacturing environment and improve the quality of its decisions. We implement the proposed system on a job shop problem, with the objective of minimising average tardiness, to evaluate its performance. Simulation results indicate that the performance of the proposed system is considerably better than other simulation-based single-pass and multi-pass scheduling algorithms available in the literature. We also illustrate knowledge extraction by presenting a sample decision tree from our experiments.en_US
dc.language.isoEnglishen_US
dc.source.titleInternational Journal of Production Researchen_US
dc.relation.isversionofhttp://dx.doi.org/10.1080/00207540903307581en_US
dc.subjectAdaptive controlen_US
dc.subjectData miningen_US
dc.subjectSimulationen_US
dc.subjectDispatching rulesen_US
dc.subjectDynamic schedulingen_US
dc.subjectGame theoryen_US
dc.subjectInventory managementen_US
dc.subjectPricing theoryen_US
dc.subjectRadio frequency identificationen_US
dc.subjectSchedulingen_US
dc.titleReal time selection of scheduling rules and knowledge extraction via dynamically controlled data miningen_US
dc.typeArticleen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.citation.spage6909en_US
dc.citation.epage6938en_US
dc.citation.volumeNumber48en_US
dc.citation.issueNumber23en_US
dc.identifier.doi10.1080/00207540903307581en_US
dc.publisherTaylor & Francisen_US
dc.identifier.eissn1366-588X


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