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Automating warm-up length estimation

Author

Listed:
  • K Hoad

    (Warwick Business School, The University of Warwick)

  • S Robinson

    (Warwick Business School, The University of Warwick)

  • R Davies

    (Warwick Business School, The University of Warwick)

Abstract

There are two key issues in assuring the accuracy of estimates of performance obtained from a simulation model. The first is the removal of any initialisation bias, the second is ensuring that enough output data is produced to obtain an accurate estimate of performance. This paper is concerned with the first issue, and more specifically warm-up estimation. Our aim is to produce an automated procedure, for inclusion into commercial simulation software, for estimating the length of warm-up and hence removing initialisation bias from simulation output data. This paper describes the extensive literature search that was carried out in order to find and assess the various existing warm-up methods, the process of short-listing and testing of candidate methods. In particular it details the extensive testing of the warm-up MSER-5 method.

Suggested Citation

  • K Hoad & S Robinson & R Davies, 2010. "Automating warm-up length estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(9), pages 1389-1403, September.
  • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:9:d:10.1057_jors.2009.87
    DOI: 10.1057/jors.2009.87
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    References listed on IDEAS

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    1. David Goldsman & Lee W. Schruben & James J. Swain, 1994. "Tests for transient means in simulated time series," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(2), pages 171-187, March.
    2. R. W. Conway, 1963. "Some Tactical Problems in Digital Simulation," Management Science, INFORMS, vol. 10(1), pages 47-61, October.
    3. Ockerman, Daniel H. & Goldsman, David, 1999. "Student t-tests and compound tests to detect transients in simulated time series," European Journal of Operational Research, Elsevier, vol. 116(3), pages 681-691, August.
    4. Lada, Emily K. & Wilson, James R., 2006. "A wavelet-based spectral procedure for steady-state simulation analysis," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1769-1801, November.
    5. Lee W. Schruben, 1982. "Detecting Initialization Bias in Simulation Output," Operations Research, INFORMS, vol. 30(3), pages 569-590, June.
    6. Mark A. Gallagher & Kenneth W. Bauer, Jr. & Peter S. Maybeck, 1996. "Initial Data Truncation for Univariate Output of Discrete-Event Simulations Using the Kalman Filter," Management Science, INFORMS, vol. 42(4), pages 559-575, April.
    7. L. Schruben & H. Singh & L. Tierney, 1983. "Optimal Tests for Initialization Bias in Simulation Output," Operations Research, INFORMS, vol. 31(6), pages 1167-1178, December.
    8. Sheth-Voss, Pieter A. & Willemain, Thomas R. & Haddock, Jorge, 2005. "Estimating the steady-state mean from short transient simulations," European Journal of Operational Research, Elsevier, vol. 162(2), pages 403-417, April.
    9. Roth, Emily, 1994. "The relaxation time heuristic for the initial transient problem in M/M/k queueing systems," European Journal of Operational Research, Elsevier, vol. 72(2), pages 376-386, January.
    10. Sandikci, Burhaneddin & Sabuncuoglu, Ihsan, 2006. "Analysis of the behavior of the transient period in non-terminating simulations," European Journal of Operational Research, Elsevier, vol. 173(1), pages 252-267, August.
    11. George S. Fishman, 1971. "Estimating Sample Size in Computing Simulation Experiments," Management Science, INFORMS, vol. 18(1), pages 21-38, September.
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