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Simulation output analysis: truncation point estimation using multiple replications in parallel
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Source Winter Simulation Conference archive
Proceedings of the 35th conference on Winter simulation: driving innovation table of contents
New Orleans, Louisiana
SESSION: Analysis methodology table of contents
Pages: 414 - 421  
Year of Publication: 2003
ISBN:0-7803-8132-7
Authors
Falko Bause  Universität Dortmund, Dortmund, Germany
Mirko Eickhoff  Universität Dortmund, Dortmund, Germany
Sponsors
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
NIST : National Institute of Standards and Technology
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
ACM: Association for Computing Machinery
(SCS) : The Society for Modeling and Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association
Publisher
Winter Simulation Conference 
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Downloads (6 Weeks): 1,   Downloads (12 Months): 5,   Citation Count: 2
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ABSTRACT

In steady-state simulation the output data of the transient phase often causes a bias in the estimation of the steady-state results. A common advice is to cut off this transient phase. Finding an appropriate truncation point is a well-known problem and is still not completely solved. In this paper we consider two algorithms for the determination of the truncation point. Both are based on a technique which takes the definition of the steady-state phase more closely into consideration. The capabilities of the algorithms are demonstrated by comparisons with two methods most often used in practice.


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
Bause, F., and H. Beilner. 1999. Intrinsic problems in simulation of logistic networks. Proc. of the 11th European Simulation Symposium and Exhibition (ESS99): 193--198.
 
2
Bause, F., and M. Eickhoff. 2002. Initial transient period detection using parallel replications. Proc. of the 14th European Simulation Symposium: 85--92.
 
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Ewing, G., K. Pawlikowski, and D. McNickle. 1999. Akaroa-2: Exploiting network computing by distributing stochastic simulation. Proc. of the 1999 European Simulation Multiconf: 175--181.
 
5
Fishman, G. S. 2001. Discrete-event simulation. Springer. R@<6>R@<6>Gafarian, A. V., C. J. Ancker, and T. Morisaku. 1978. Evaluation of commonly used rules for detecting steady state in computer simulation. Naval Research Logistics Quarterly 78:511--529.
 
7
Hamilton, J. D. 1994. Time series analysis. Princeton University Press.
 
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Hartung, J., B. Elpelt, and K. -H. Klösener. 1985. Statistik. R. Oldenbourg Verlag.
 
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Welch, P. D. 1983. The statistical analysis of simulation results. In The Computer Performance Modeling Handbook, ed. S. Lavenberg, Academic Press: 268--328.

Collaborative Colleagues:
Falko Bause: colleagues
Mirko Eickhoff: colleagues