Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/13540
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRoy, Debjit
dc.contributor.authorGupta, Akash
dc.contributor.authorPazour, Jennifer A.
dc.date.accessioned2015-05-13T09:39:50Z
dc.date.available2015-05-13T09:39:50Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/11718/13540
dc.description.abstractQueue length distributions provide insight into the impact of service system design changes that go beyond simple performance measure averages; however, such distributions are difficult to estimate when service times are not exponential. In this research, we model service systems using queuing networks and develop a continuous time Markov chain (CTMC) to compute the steady state probability distribution function for the number of customers and the waiting time probabilities in a network of GI/G/c queues. Using a generalised generator matrix, we evaluate the steady state probability of any number of customers in the queue. For a network of general queues, we link the queues using a parametric decomposition approach. Through two service sector examples, we illustrate that explicitly modelling the arrival and service rates as general distributions (rather than approximating them using Markovian distributions) can lead to significantly better resource allocations.
dc.language.isoenen_US
dc.publisherInternational. Journal. of Automation and Logisticsen_US
dc.subjectTime Probabilitiesen_US
dc.titleImproving service system performance using estimates of waiting time probabilitiesen_US
dc.typeArticleen_US
Appears in Collections:Journal Articles

Files in This Item:
File Description SizeFormat 
Improving service system performance using.pdf
  Restricted Access
647.96 kBAdobe PDFView/Open Request a copy


Items in IIMA Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated.