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Deducing Queueing From Transactional Data: The Queue Inference Engine, Revisited

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dc.creator Bertsimas, Dimitris J.
dc.creator Servi, Les D.
dc.date 2004-05-28T19:27:14Z
dc.date 2004-05-28T19:27:14Z
dc.date 1990-04
dc.date.accessioned 2013-10-09T02:38:15Z
dc.date.available 2013-10-09T02:38:15Z
dc.date.issued 2013-10-09
dc.identifier http://hdl.handle.net/1721.1/5190
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description Larson [1] proposed a method to statistically infer the expected transient queue length during a busy period in 0(n 5 ) solely from the n starting and stopping times of each customer's service during the busy period and assuming the arrival distribution is Poisson. We develop a new O(n3 ) algorithm which uses this data to deduce transient queue lengths as well as the waiting times of each customer in the busy period. We also develop an O(n) on line algorithm to dynamically update the current estimates for queue lengths after each departure. Moreover we generalize our algorithms for the case of time-varying Poisson process and also for the case of iid interarrival times with an arbitrary distribution. We report computational results that exhibit the speed and accuracy of our algorithms.
dc.format 1744 bytes
dc.format 940553 bytes
dc.format application/pdf
dc.language en_US
dc.publisher Massachusetts Institute of Technology, Operations Research Center
dc.relation Operations Research Center Working Paper;OR 212-90
dc.title Deducing Queueing From Transactional Data: The Queue Inference Engine, Revisited
dc.type Working Paper


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