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dc.contributor.authorWu, Cheng-Hung
dc.contributor.authorZhou, Fang-Yi
dc.contributor.authorTsai, Chi-Kang
dc.contributor.authorYu, Cheng-Juei
dc.contributor.authorDauzère-Pérès, Stéphane
dc.date.accessioned2022-01-04T13:44:07Z
dc.date.available2022-01-04T13:44:07Z
dc.date.created2020-07-16T11:22:55Z
dc.date.issued2020
dc.identifier.citationInternational Journal of Production Research. 2020, 58 (9), 2822-2840.en_US
dc.identifier.issn0020-7543
dc.identifier.urihttps://hdl.handle.net/11250/2836015
dc.description.abstractThis research combines deep neural network (DNN) and Markov decision processes (MDP) for the dynamic dispatching of re-entrant production systems. In re-entrant production systems, jobs enter the same workstation multiple times and dynamic dispatching oftentimes aims to dynamically assign different priorities to various job groups to minimise weighted cycle time or maximise throughput. MDP is an effective tool for dynamic production control, but it suffers from two major challenges in dynamic control problems. First, the curse of dimensionality limits the computational performance of solving large MDP problems. Second, a different model should be built and solved after system configuration is changed. DNN is used to overcome both challenges by learning directly from optimal dispatching policies generated by MDP. Results suggest that a properly trained DNN model can instantly generate near-optimal dynamic control policies for large problems. The quality of the DNN solution is compared with the optimal dynamic control policies through the standard K-fold cross-validation test and discrete event simulation. On average, the performance of the DNN policy is within 2% of optimal in both tests. The proposed artificial intelligence algorithm illustrates the potential of machine learning methods in manufacturing applications.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.subjectDynamic dispatchingen_US
dc.subjectMarkov decision processesen_US
dc.subjectDeep learningen_US
dc.subjectDeep neural networken_US
dc.titleA deep learning approach for the dynamic dispatching of unreliable machines in re-entrant production systemsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber2822-2840en_US
dc.source.volume58en_US
dc.source.journalInternational Journal of Production Researchen_US
dc.source.issue9en_US
dc.identifier.doi10.1080/00207543.2020.1727041
dc.identifier.cristin1819566
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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