Statistical Analysis with Little's Law

Song-Hee Kim () and Ward Whitt ()
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Song-Hee Kim: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027
Ward Whitt: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027,

Operations Research, 2013, vol. 61, issue 4, 1030-1045

Abstract: The theory supporting Little's Law (L=(lambda)W) is now well developed, applying to both limits of averages and expected values of stationary distributions, but applications of Little's Law with actual system data involve measurements over a finite-time interval, which are neither of these. We advocate taking a statistical approach with such measurements. We investigate how estimates of L and (lambda) can be used to estimate W when the waiting times are not observed. We advocate estimating confidence intervals. Given a single sample-path segment, we suggest estimating confidence intervals using the method of batch means, as is often done in stochastic simulation output analysis. We show how to estimate and remove bias due to interval edge effects when the system does not begin and end empty. We illustrate the methods with data from a call center and simulation experiments.

Keywords: Little's Law; L=(lambda)W; measurements; parameter estimation; confidence intervals; bias; finite-time versions of Little's Law; confidence intervals with L=(lambda)W; edge effects in L=(lambda)W; performance analysis (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (8)

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