Chun-Hung Chen
ABSTRACT
Simulation plays a vital role in analyzing many discrete event systems. Usually, using simulation to solve such problems can be both expensive and time consuming. We present an effective approach to smartly allocate computing budget for discrete-event simulation. This approach can smartly determine the best simulation lengths for all simulation experiments and significantly reduce the total computation cost for obtaining the same confidence level. Numerical testing shows that our approach can obtain the same simulation quality with one-tenth the simulation effort.
- Banks, J., and J. S. Carson, B. L. Nelson. 1995. Discrete-Event System Simulation, Prentice-Hall.Google Scholar
- Bechhofer, R. E., T. J. Santner, and D. M. Goldsman. 1995. Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons, John Wiley & Sons, Inc.Google Scholar
- Casella, G., and R. L. Berger. 1990. Statistical Inference, Wadsworth.Google Scholar
- Chen, C. H. 1995. "An Effective Approach to Smartly Allocate Computing Budget for Discrete Event Simulation," Proceedings of the 34th 1EEE Conference on Decision and Control, 2598-2605. Google ScholarDigital Library
- Chen, C. H. 1996. "A Lower Bound for the Correct Subset-Selection Probability and Its Application to Discrete Event System Simulations," To appear on IEEE Transactions on Automatic Control.Google Scholar
- Chen, C. H., and Y. C. Ho. 1995. "An Approximation Approach of the Standard Clock Method for General Discrete Event Simulation," IEEE Transactions on Control Systems Technology, Vol. 3, #3, 309-317.Google ScholarCross Ref
- Goldsman, G., and B. L. Nelson. 1994. "Ranking, Selection, and Multiple Comparison in Computer Simulation," Proceedings of the 1994 Winter Simulation Conference, 192-199. Google ScholarDigital Library
- Goldsman, G., B. L. Nelson, and B. Schmeiser. 1991. "Methods for Selecting the Best System," Proceedings of the 1991 Winter Simulation Conference, 177-186. Google ScholarDigital Library
- Gupta, S. S. and S. Panchapakesan. 1979. Multiple Decision Procedures: Theory and Methodology of Selecting and Ranking Populations, John Wiley. Google ScholarDigital Library
- Ho, Y. C. (Editor). 1991. Discrete Event Dynamic Systems, IEEE Press.Google Scholar
- Ho, Y. C., R. S. Sreenivas, and P. Vakili. 1992. "Ordinal Optimization of DEDS", Journal of Discrete Event Dynamic Systems, 2, #2, 61-88.Google ScholarCross Ref
- Law, A. M. and W. D. Kelton. 1991. Simulation Modeling & Analysis, McGraw-Hill, Inc. Google ScholarDigital Library
- Luenberger, D. G. 1984. Linear and Nonlinear Programming, Addison-Wesley.Google Scholar
- Ross, S. 1994. A First Course in Probability, Prentice Hall.Google Scholar
- Vakili, P. 1991. "A Standard Clock Technique for Efficient Simulation, "Operations Research Letters, Vol. 10, 445-452.Google Scholar
Recommendations
Inside discrete-event simulation software: how it works and why it matters
WSC '10: Proceedings of the Winter Simulation ConferenceThis paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; ...
Inside discrete-event simulation software: how it works and why it matters
WSC '13: Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex WorldThis paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; ...
Inside discrete-event simulation software: how it works and why it matters
WSC '09: Winter Simulation ConferenceThis paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; ...
Comments