DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorWang, Pen_US
dc.creatorPan, Ken_US
dc.creatorYan, Zen_US
dc.creatorLim, YFen_US
dc.date.accessioned2021-08-13T06:13:33Z-
dc.date.available2021-08-13T06:13:33Z-
dc.identifier.issn1059-1478en_US
dc.identifier.urihttp://hdl.handle.net/10397/90651-
dc.language.isoenen_US
dc.publisherWiley-Blackwellen_US
dc.subjectBucket brigadeen_US
dc.subjectStochasti c service timeen_US
dc.subjectProductivityen_US
dc.subjectVariabilityen_US
dc.titleManaging stochastic bucket brigades on discrete work stationsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1111/poms.13539en_US
dcterms.abstractBucket brigades are notably used to coordinate workers in production systems. We study a J-station, I-worker bucket brigade system. The time duration for each worker to serve a job at a station is exponentially distributed with a rate that depends on the station's expected work content and the worker's work speed. Our goal is to maximize the system's productivity or to minimize its inter-completion time variability. We analytically derive the throughput and the coefficient of variation (CV) of the inter-completion time. We study the system under two cases. (i) If the work speeds depend only on the workers, the throughput gap between the stochastic and the deterministic systems can be up to 47% when the number of stations is small. Either maximizing the throughput or minimizing the CV of the inter-completion time, the slowest-to-fastest worker sequence always outperforms the reverse sequence for the stochastic bucket brigade. To maximize the throughput, more work content should be assigned to the stations near the faster workers. In contrast, to minimize the CV of the inter-completion time, more work content should be allocated to the stations near the slower workers. (ii) If the work speeds depend on the workers and the stations such that the workers may not dominate each other at every station, the asymptotic throughput can be expressed as a function of the average work speeds and the asymptotic expected blocked times of the workers, and can be interpreted as the sum of the effective production rates of all the workers.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationProduction and operations management, 2021, Accepted, https://doi.org/10.1111/poms.13539en_US
dcterms.isPartOfProduction and operations managementen_US
dcterms.issued2021-
dc.identifier.eissn1937-5956en_US
dc.description.validate202108 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1002-n01-
dc.identifier.SubFormID2401-
dc.description.fundingSourceRGCen_US
dc.description.fundingText15501920en_US
dc.description.pubStatusEarly releaseen_US
dc.date.embargo0000-00-00 (to be updated)en_US
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