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Transaction tagging in highly congested queueing simulations

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Abstract

The purpose of this paper is to illustrate how a very simple queueing model can be used to gain insight into a computer memory management strategy that is important for a large class of discrete-event simulation models. To this end, an elementary queueing model is used to demonstrate that it can be advantageous to run transaction-based simulations with a relatively few “tagged” transactions that collect data. The remaining transactions merely congest the system. Conceptually the tagged transactions flow through the simulation acting similar to radioactive trace elements inserted into a biological system. The queueing model analyzed in this paper provides insight into some trade-offs in simulation data collection. We show that, while resulting in a longer computer run, an optimal tagging interval greater than one will minimize the probability of prematurely aborting the run. Finally, we propose a heuristic procedure to estimate the optimal tagging interval. We illustrate this with an actual simulation study of a steel production facility.

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Authors and Affiliations

  1. School of Operations Research and Industrial Engineering Cornell University, 14853, Ithaca, NY, USA

    Lee W. Schruben & Enver Yucesan

Authors
  1. Lee W. Schruben
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  2. Enver Yucesan
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Additional information

This research was partially supported by a grant to Cornell University by the Bethlehem Steel Corporation

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Schruben, L.W., Yucesan, E. Transaction tagging in highly congested queueing simulations. Queueing Syst 3, 257–264 (1988). https://doi.org/10.1007/BF01161217

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  • DOI: https://doi.org/10.1007/BF01161217

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