More Web Proxy on the site http://driver.im/

research-article

Automating warm-up length estimation

Authors:

Stewart Robinson,

Ruth DaviesAuthors Info & Claims

WSC '08: Proceedings of the 40th Conference on Winter Simulation

Pages 532 - 540

Published: 07 December 2008 Publication History

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. A continuing research project is described that aims 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.

References

[1]

Alexopoulos, C., and A. F. Seila. 1998. Output data analysis, Handbook of simulation, 225--272. New York: Wiley.

[2]

Banks, J., J. S. Carson, B. L. Nelson, and D. M. Nicol. 2001. Discrete-event system simulation. 4th ed. New Jersey: Prentice-Hall.

[3]

Bause, F., and M. Eickhoff. 2003. Truncation point estimation using multiple replications in parallel. In Proceedings of the 2003 Winter Simulation Conference, 414--421.

Digital Library

[4]

Beck, A. D. 2004. Consistency of warm up periods for a simulation model that is cyclic in nature. In Proceedings of the Simulation Study Group, OR Society, 105--108.

[5]

Box, G. E., G. M. Jenkins, and G. C. Reinsel. 1994. Time series analysis:forecasting and control, 3rd ed. New Jersey: Prentice-Hall.

Digital Library

[6]

Bratley, P., B. Fox, and L. Schrage. 1987. A guide to simulation, 2nd ed. New York: Springer-Verlag.

Digital Library

[7]

Cash, C. R., D. G. Dippold, J. M. Long, and W. P. Pollard. 1992. Evaluation of tests for initial-condition bias. In Proceedings of the 1992 Winter Simulation Conference, 577--585.

Digital Library

[8]

Conway, R. W. 1963. Some tactical problems in digital simulation. Management Science 10(1): 47--61.

Digital Library

[9]

Delaney, P. J. 1995. Control of initialisation bias in queuing simulations using queuing approximations. M.S. thesis, Department of Systems Engineering, University of Virginia.

[10]

Fishman, G. S. 1971. Estimating sample size in computing simulation experiments Management Science 18: 21--38.

[11]

Fishman, G. S. 1973. Concepts and methods in discrete event digital simulation. New York: Wiley.

[12]

Fishman, G. S. 2001. Discrete-event simulation, modeling, programming, and analysis. New York: Springer-Verlag.

Digital Library

[13]

Gafarian, A. V., C. J. Ancker Jnr, and T. Morisaku. 1978. Evaluation of commonly used rules for detecting 'steady state' in computer simulation. Naval Research Logistics Quarterly 25: 511--529.

[14]

Gallagher, M. A., K. W. Bauer Jnr, and P. S. Maybeck. 1996. Initial data truncation for univariate output of discrete-event simulations using the Kalman Filter. Management Science 42(4): 559--575.

Digital Library

[15]

Glynn, P. W., and D. L. Iglehart. 1987. A New Initial Bias Deletion rule. In Proceedings of the 1987 Winter Simulation Conference, 318--319.

Digital Library

[16]

Goldsman, D., L. W. Schruben, and J. J. Swain. 1994. Tests for transient means in simulated time series. Naval Research Logistics 41: 171--187.

[17]

Gordon, G. 1969. System simulation. New Jersey: Prentice-Hall.

Digital Library

[18]

Jackway, P. T., and B. M deSilva. 1992. A methodology for initialisation bias reduction in computer simulation output. Asia-Pacific Journal of Operational Research 9: 87--100.

[19]

Kelton, W. D., and A. M. Law. 1983. A new approach for dealing with the startup problem in discrete event simulation. Naval Research Logistics Quarterly. 30: 641--658.

[20]

Kimbler, D. L., and B. D. Knight. 1987. A survey of current methods for the elimination of initialisation bias in digital simulation. Annual Simulation Symposium 20: 133--142.

Digital Library

[21]

Lada, E. K., and J. R. Wilson. 2006. A wavelet-based spectral procedure for steady-state simulation analysis European Journal of Operational Research 174: 1769--1801.

[22]

Lada, E. K., J. R. Wilson, and N. M. Steiger. 2003. A wavelet-based spectral method for steady-state simulation analysis. In Proceedings of the 2003 Winter Simulation Conference, 422--430.

Digital Library

[23]

Lada, E. K., J. R. Wilson, N. M. Steiger, and J. A. Joines. 2004. Performance evaluation of a wavelet-based spectral method for steady-state simulation analysis. In Proceedings of the 2004 Winter Simulation Conference, 694--702.

Digital Library

[24]

Law, A. M., and W. D. Kelton. 2000. Simulation modelling and analysis. New York: McGraw-Hill.

Digital Library

[25]

Law, A. M. 1983. Statistical analysis of simulation output data. Operations Research 31: 983--1029.

Digital Library

[26]

Lee, Y-H., and H-S. Oh. 1994. Detecting truncation point in steady-state simulation using chaos theory. In Proceedings of the 1994 Winter Simulation Conference, 353--360.

Digital Library

[27]

Lee, Y-H., K-H. Kyung, and C-S. Jung. 1997. On-line determination of steady state in simulation outputs. Computers industrial engineering 33(3): 805--808.

[28]

Linton, J. R., and C. M. Harmonosky. 2002. A comparison of selective initialization bias elimination methods. In Proceedings of the Winter Simulation Conference, 1951--1957.

Digital Library

[29]

Ma, X., and A. K. Kochhar. 1993. A comparison study of two tests for detecting initialization bias in simulation output. Simulation 61(2): 94--101.

[30]

Mahajan, P. S., and R. G. Ingalls. 2004. Evaluation of methods used to detect warm-up period in steady state simulation. In Proceedings of the 2004 Winter Simulation Conference, 663--671.

Digital Library

[31]

Nelson, B. L. 1992. Statistical analysis of simulation results, Handbook of industrial engineering. 2nd ed. New York: John Wiley.

[32]

Ockerman, D. H., and D. Goldsman. 1999. Student t-tests and compound tests to detect transients in simulated time series. European Journal of Operational Research 116: 681--691.

[33]

Pawlikowski, K. 1990. Steady-state simulation of queueing processes: A survey of problems and solutions. Computing Surveys 122(2): 123--170.

Digital Library

[34]

Robinson, S. 2004. Simulation. The practice of model development and use. England: John Wiley & Sons Ltd.

Digital Library

[35]

Robinson, S. 2005. A statistical process control approach to selecting a warm-up period for a discrete-event simulation. European Journal of Operational Research 176: 332--346.

[36]

Rossetti, M. D., and P. J. Delaney. 1995. Control of initialization bias in queueing simulations using queueing approximations. In Proceedings of the 1995 Winter Simulation Conference, 322--329.

Digital Library

[37]

Rossetti, M. D., Z. Li, and P. Qu. 2005. Exploring exponentially weighted moving average control charts to determine the warm-up period. In Proceedings of the Winter Simulation Conference, 771--780.

Digital Library

[38]

Roth, E., and N. Josephy. 1993. A relaxation time heuristic for exponential-Erlang queueing systems. Computers & Operations research 20(3): 293--301.

Digital Library

[39]

Roth, E. 1994. The relaxation time heuristic for the initial transient problem in M/M/k queueing systems. European Journal of Operational Research. 72: 376--386.

[40]

Sandikci, B., and I. Sabuncuoglu. 2006. Analysis of the behaviour of the transient period in non-terminating simulations European Journal of Operational Research 173: 252--267.

[41]

Schruben, L. W. 1982. Detecting initialization bias in simulation output. Operations Research 30(3): 569--590.

Digital Library

[42]

Schruben, L., H. Singh, and L. Tierney. 1983. Optimal tests for initialization bias in simulation output. Operations Research 31(6): 1167--1178.

Digital Library

[43]

Sheth-Voss, P. A., T. R. Willemain, and J. Haddock. 2005. Estimating the steady-state mean from short transient simulations. European Journal of Operational Research 162(2): 403--417.

[44]

Spratt, S. C. 1998. An evaluation of contemporary heuristics for the startup problem. M. S. thesis, Faculty of the School of Engineering and Applied Science, University of Virginia.

[45]

Vassilacopoulos, G. 1989. Testing for initialization bias in simulation output. Simulation 52(4): 151--153.

[46]

White Jnr, K. P. 1997. An effective truncation heuristic for bias reduction in simulation output. Simulation 69(6): 323--334.

Digital Library

[47]

White Jnr, K. P., M. J. Cobb, and S. C. Spratt. 2000. A comparison of five steady-state truncation heuristics for simulation. In Proceedings of the 2000 Winter Simulation Conference, 755--760.

Digital Library

[48]

Wilson, J. R., and A. A. B. Pritsker. 1978a. A survey of research on the simulation startup problem. Simulation 31(2): 55--58.

[49]

Wilson, J. R., and A. A. B. Pritsker. 1978b. Evaluation of startup policies in simulation experiments. Simulation 31(3): 79--89.

[50]

Yucesan, E. 1993. Randomization tests for initialization bias in simulation output. Naval Research Logistics 40: 643--663.

[51]

Zobel, C. W., and K. P. White Jnr 1999. Determining a warm-up period for a telephone network routing simulation. In Proceedings of the 1999 Winter Simulation Conference, 662--665.

Digital Library

Cited By

White KHwang S(2015)Delay times in an M/M/1 queueProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888674(493-504)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888674
Khalili MZahedi FHill RKuhl M(2013)Modeling and simulation of a mattress production line using promodelProceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World10.5555/2675983.2676305(2598-2609)Online publication date: 8-Dec-2013
https://dl.acm.org/doi/10.5555/2675983.2676305
Sánchez PWhite K(2011)Interval estimation using replication/deletion and MSER truncationProceedings of the Winter Simulation Conference10.5555/2431518.2431574(488-494)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2431574
Show More Cited By

Recommendations

Simulation Model, Warm-up Period, and Simulation Length of Cellular Systems
ISMS '11: Proceedings of the 2011 Second International Conference on Intelligent Systems, Modelling and Simulation

In this paper, discrete event simulation by batch-means of a M/M/∞ queuing system is utilised to simulate a cellular CDMA system. The details of the simulation model, warm-up period, and simulation run time are discussed. The warm-up period is studied ...
Covariance shaping least-squares estimation

A new linear estimator is proposed, which we refer to as the covariance shaping least-squares (CSLS) estimator, for estimating a set of unknown deterministic parameters, x, observed through a known linear transformation H and corrupted by additive ...
Rethinking Biased Estimation: Improving Maximum Likelihood and the Cramér–Rao Bound

One of the prime goals of statistical estimation theory is the development of performance bounds when estimating parameters of interest in a given model, as well as constructing estimators that achieve these limits. When the parameters to be estimated ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WSC '08: Proceedings of the 40th Conference on Winter Simulation

December 2008

3189 pages

ISBN:9781424427086

Editors:
Scott Mason
University of Arkansas
,
Ray Hill
Air Force Institute of Technology
,
Lars Mönch
University of Hagen
,
Oliver Rose
University of Dresden
,
General Chair:
Thomas Jefferson
Intel Corporation
,
Program Chairs:
John W. Fowler
Arizona State University
,
Ricki G. Ingalls
Oklahoma State University

Sponsors

IIE: Institute of Industrial Engineers
INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
ASA: American Statistical Association
IEEE/SMC: Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling
NIST: National Institute of Standards and Technology
(SCS): The Society for Modeling and Simulation International

Publisher

Winter Simulation Conference

Publication History

Published: 07 December 2008

Check for updates

Qualifiers

Research-article

Conference

WSC08

Sponsor:

IIE
INFORMS-SIM
ASA
IEEE/SMC
SIGSIM
NIST
(SCS)

WSC08: Winter Simulation Conference

December 7 - 10, 2008

Florida, Miami

Acceptance Rates

WSC '08 Paper Acceptance Rate 249 of 304 submissions, 82%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
152
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 31 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

White KHwang S(2015)Delay times in an M/M/1 queueProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888674(493-504)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888674
Khalili MZahedi FHill RKuhl M(2013)Modeling and simulation of a mattress production line using promodelProceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World10.5555/2675983.2676305(2598-2609)Online publication date: 8-Dec-2013
https://dl.acm.org/doi/10.5555/2675983.2676305
Sánchez PWhite K(2011)Interval estimation using replication/deletion and MSER truncationProceedings of the Winter Simulation Conference10.5555/2431518.2431574(488-494)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2431574
Pasupathy RSchmeiser BYücesan E(2010)The initial transient in steady-state point estimationProceedings of the Winter Simulation Conference10.5555/2433508.2433529(184-197)Online publication date: 5-Dec-2010
https://dl.acm.org/doi/10.5555/2433508.2433529

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents