Automating DES output analysis

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Automating DES output analysis: how many replications to run

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Winter Simulation Conference archive
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come table of contents

Washington D.C.

SESSION: Analysis methodology B: recent advances in optimization and analysis table of contents

Pages 505-512

Year of Publication: 2007

ISBN:1-4244-1306-0

Authors

Kathryn Hoad	University of Warwick, Coventry, U.K.
Stewart Robinson	University of Warwick, Coventry, U.K.
Ruth Davies	University of Warwick, Coventry, U.K.

Sponsors

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

Publisher

IEEE Press Piscataway, NJ, USA

Bibliometrics

Downloads (6 Weeks): 2, Downloads (12 Months): 14, Citation Count: 0

Additional Information:

abstract references collaborative colleagues

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ABSTRACT

This paper describes the selection and automation of a method for estimating how many replications should be run to achieve a required accuracy in the output. The motivation is to provide an easy to use method that can be incorporated into existing simulation software that enables practitioners to obtain results of a specified accuracy. The processes and decisions involved in selecting and setting up a method for automation are explained. The extensive test results are outlined, including results from applying the algorithm to a collection of artificial and real models.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Banks, J., J. S. Carson II, B. L. Nelson, and D. M. Nicol. 2005. Discrete-event system simulation. 4th ed. Pearson Prentice Hall.
	2	Cheng, R. C. H. 2006. Validating and comparing simulation models using resampling. Journal Of Simulation 1(1).
	3	Hlupic, V. 1999. Discrete-event simulation software: What the users want. Simulation 73 (6):362--370.
	4	Hollocks, B. W. 2001. Discrete-event simulation: An inquiry into user practice. Simulation Practice and Theory 8:451--471.
	5	Averill M. Law , W. David Kelton, Simulation Modeling and Analysis, McGraw-Hill Higher Education, 1997
	6	Law, A M. and M. McComas. 1990. Secrets of successful simulation studies. Industrial Engineering 22(5):47--72.
	7	Stewart Robinson, Simulation: The Practice of Model Development and Use, John Wiley & Sons, 2004
	8	Shao, J and D. Tu. 2006. The Jackknife and Bootstrap (Springer Series in Statistics). Springer-Verlag.

Collaborative Colleagues:

Kathryn Hoad: colleagues

Stewart Robinson: colleagues

Ruth Davies: colleagues

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