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

Dimitris J. Bertsimas and L. D. Servi
Additional contact information
Dimitris J. Bertsimas: Massachusetts Institute of Technology, Cambridge, Massachusetts
L. D. Servi: GTE Laboratories, Incorporated, Waltham, Massachusetts

Operations Research, 1992, vol. 40, issue 3-supplement-2, S217-S228

Abstract: R. Larson proposed a method to statistically infer the expected transient queue length during a busy period in O ( 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 ( n 3 ) algorithm which uses these 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 a time-varying Poisson process and also for the case of i.i.d. interarrival times with an arbitrary distribution. We report computational results that exhibit the speed and accuracy of our algorithms.

Keywords: queues: statistical inference; algorithms; transient results (search for similar items in EconPapers)
Date: 1992
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.40.3.S217 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:40:y:1992:i:3-supplement-2:p:s217-s228

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().