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dc.contributor.authorYang, K. K.
dc.contributor.authorÇayırlı, Tuğba
dc.contributor.authorLow, J. M.W.
dc.date.accessioned2016-07-29T05:25:55Z
dc.date.available2016-07-29T05:25:55Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10679/4311
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S030505481630137X
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractExisting models of multi-server queues with system transience and non-standard assumptions are either too complex or restricted in their assumptions to be used broadly in practice. This paper proposes using data analytics, combining computer simulation to generate the data and an advanced non-linear regression technique called the Alternating Conditional Expectation (ACE) to construct a set of easy-to-use equations to predict the performance of queues with a scheduled start and end time. Our results show that the equations can accurately predict the queue performance as a function of the number of servers, mean arrival load, session length and service time variability. To further facilitate its use in practice, the equations are developed into an open-source online tool accessible at http://singlequeuesystemstool.com/. The proposed procedure of data analytics can be used to model other more complex systems.
dc.language.isoengen_US
dc.publisherElsevier
dc.relation.ispartofComputers & Operations Research
dc.rightsrestrictedAccess
dc.titlePredicting the performance of queues–A data analytic approachen_US
dc.typeArticleen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0001-7515-8716 & YÖK ID 26584) Çayırlı, Tuğba
dc.contributor.ozuauthorÇayırlı, Tuğba
dc.identifier.volume76
dc.identifier.startpage33
dc.identifier.endpage42
dc.identifier.wosWOS:000383008100004
dc.identifier.doi10.1016/j.cor.2016.06.005
dc.subject.keywordsData analytics for queues
dc.subject.keywordsSimulation
dc.subject.keywordsNonlinear regression
dc.subject.keywordsAlternating conditional expectation
dc.identifier.scopusSCOPUS:2-s2.0-84976591686
dc.contributor.authorFemale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff


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