An asymptotic allocation for simultaneous simulation experiments

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

An asymptotic allocation for simultaneous simulation experiments

Full text

Pdf (105 KB)

Source

Winter Simulation Conference archive
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1 table of contents

Phoenix, Arizona, United States

Pages: 359 - 366

Year of Publication: 1999

ISBN:0-7803-5780-9

Authors

Hsiao-Chang Chen	Dept. of Systems Engineering, University of Pennsylvania, Philadelphia, PA
Chun-Hung Chen
Jianwu Lin	Dept. of Systems Engineering, University of Pennsylvania, Philadelphia, PA
Enver Yücesan	INSEAD, Technology Management Area, Fontainebleau, France

Sponsors

ACM: Association for Computing Machinery
SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 0, Downloads (12 Months): 8, Citation Count: 7

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/324138.324242 What is a DOI?

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	Bechhofer, R. E., C. W. Dunnett, and M. Sobel 1954. A two-sample multiple decision procedure for ranking means of normal populations with a common unknown variance. Biometrika, 41, 170-176.
	2	Bechhofer, R. E. 1969. Optimal allocation of observations when comparing several treatments with a control. In Multivariate Analysis II, P. R. Krishnaiah, ed., Academic Press, Inc. 463-473. Berger
	3	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.
	4	Berger, J. O., and J. Deely. 1994. "A Bayesian Approach to Ranking and Selection of Related Means with Alternative to AOV Methodology," Journal of American Statistics Association. 83, 364-373.
	5	Bernardo, J. M., and A. F. M. Smith. 1994. Bayesian Theory. John Wiley.
	6	Chen, C. H., 1995. An Effective Approach to Smartly Allocate Computing Budget for Discrete Event Simulation. Proceedings of the 34th IEEE Conference on Decision and Control, 2598-2605.
	7	Chen, C. H. 1996. "A Lower Bound for the Correct Subset-Selection Probability and Its Application to Discrete Event System Simulations." IEEE Transactions on Automatic Control, 41:1227-1231.
	8	Chun-Hung Chen , Hsiao-Chang Chen , Liyi Dai, A gradient approach for smartly allocating computing budget for discrete event simulation, Proceedings of the 28th conference on Winter simulation, p.398-405, December 08-11, 1996, Coronado, California, United States [doi>10.1145/256562.256696]
	9	Hsiao-Chang Chen , Liyi Dai , Chun-Hung Chen , Enver Yücesan, New development of optimal computing budget allocation for discrete event simulation, Proceedings of the 29th conference on Winter simulation, p.334-341, December 07-10, 1997, Atlanta, Georgia, United States [doi>10.1145/268437.268501]
	10	Chun-Hung Chen , Yu Yuan , Hsiao-Chang Chen , Enver Yücesan , Liyi Dai, Computing budget allocation for simulation experiments with different system structure, Proceedings of the 30th conference on Winter simulation, p.735-742, December 13-16, 1998, Washington, D.C., United States
	11	Chen, C. H., S. D. Wu, and L. Dai. 1999a. "Ordinal Comparison of Heuristic Algorithms Using Stochastic Optimization," IEEE Transactions on Robotics and Automation, 15: 44-56.
	12	Chen, H. C., C. H. Chen, and E. Y~cesan. 1999b. "Computing Efforts Allocation for Ordinal Optimization and Discrete Event Simulation," To appear in IEEE Transactions on Automatic Control.
	13	Stephen E. Chick, Bayesian analysis for simulation input and output, Proceedings of the 29th conference on Winter simulation, p.253-260, December 07-10, 1997, Atlanta, Georgia, United States [doi>10.1145/268437.268488]
	14	Stephen E. Chick, Selecting the best system: a decision-theoretic approach, Proceedings of the 29th conference on Winter simulation, p.326-333, December 07-10, 1997, Atlanta, Georgia, United States [doi>10.1145/268437.268499]
	15	Chick, S. E. and Inoue, K., 1998, "How to More Effectively Allocate Computing Resources to Identify The Best Simulated Alternative" working paper.
	16	Chiu, W. K., 1974. Selecting the n populations with Largest Means From k normal Populations with Unknown Variances. Australian Journal of Statistics, 16, 144-147.
	17	De Groot, M. H. 1970. Optimal Statistical Decisions. McGraw-Hill, Inc.
	18	Dudewicz, E. J. and S. R. Dalal, 1975. Allocation of Observations in Ranking and Selection with Unequal Variances. Sankhya, B37:28-78.
	19	Dunnett, C. W. and M. Sobel, 1954. A Bivariate Generalization of Student's t distribution, with Tables for Certain Special Cases, Biometrika, 41, 153-169.
	20	Futschik, A. and G. C. Pflug, 1997. Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization and Approximative Algorithms. European Journal of Operational Research, 245-260.
	21	David Goldsman , Barry L. Nelson , Bruce Schmeiser, Methods for selecting the best system, Proceedings of the 23rd conference on Winter simulation, p.177-186, December 08-11, 1991, Phoenix, Arizona, United States
	22	David Goldsman , Barry L. Nelson, Ranking, selection and multiple comparisons in computer simulations, Proceedings of the 26th conference on Winter simulation, p.192-199, December 11-14, 1994, Orlando, Florida, United States
	23	Shanti S. Panchapakesan, Multiple Decision Procedures: Theory and Methodology of Selecting and Ranking Populations, Society for Industrial and Applied Mathematics, Philadelphia, PA, 2002
	24	Gupta, S. S. and J. O. Berger, 1988. Statistical Decision Theory and Related Topics IV. Springer Verlag.
	25	Hsu, J. C., 1996. Multiple Comparisons Theory and Methods. Chapman & Hall.
	26	Koichiro Inoue , Stephen E. Chick, Comparison of Bayesian and frequentist assessments of uncertainty for selecting the best system, Proceedings of the 30th conference on Winter simulation, p.727-734, December 13-16, 1998, Washington, D.C., United States
	27	Jack P C Kleijnen, Statistical tools for simulation practitioners, Marcel Dekker, Inc., New York, NY, 1986
	28	Averill M. Law , W. David Kelton, Simulation Modeling and Analysis, McGraw-Hill Higher Education, 1997
	29	Matejcik, F. J. and B. L. Nelson, 1995. Two-stage multiple comparisons with the best for computer simulation. Operations Research, 43, 633-640.
	30	Patsis, N. T., C. H. Chen, and M. E. Larson. 1997. "SIMD Parallel Discrete Event Dynamic System Simulation," IEEE Transactions on Control Systems Technology, 5:30-41.
	31	Rinott, Y., 1978. On Two-stage Selection Procedures and Related Probability Inequalities. Communications in Statistics A7, 799-811.
	32	Ross, S. 1994. A First Course in Probability, Prentice Hall Inc.
	33	Stein, C., 1945. A two-sample test for linear hypothesis whose power is independent of the variance. The Annals of Mathematical Statistics, 16, 243-258.
	34	Tong, Y. L. and D. E. Wetzell, 1984. Allocation of Observations for Selecting The Best Normal Population. 213-224. Design of Experiments : Ranking & Selection, 213-224.

CITED BY 7

	E. Jack Chen , W. David Kelton, Comparing systems via stochastic simulation: an enhanced two-stage selection procedure, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
	Stephen E. Chick, Bayesian methods: bayesian methods for simulation, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
	Jason R. W. Merrick , Varun Dinesh , Amita Singh , J. René van Dorp , Thomas A. Mazzuchi, Simulation of large networks: propagation of uncertainty in a simulation-based maritime risk assessment model utilizing Bayesian simulation techniques, Proceedings of the 35th conference on Winter simulation: driving innovation, December 07-10, 2003, New Orleans, Louisiana
	Stephen E. Chick , Koichiro Inoue, New results on procedures that select the best system using CRN, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
	Stephen E. Chick, Bayesian methods for discrete event simulation, Proceedings of the 36th conference on Winter simulation, December 05-08, 2004, Washington, D.C.
	Geng Deng , Michael C. Ferris, Adaptation of the UOBYQA algorithm for noisy functions, Proceedings of the 37th conference on Winter simulation, December 03-06, 2006, Monterey, California
	Geng Deng , Michael C. Ferris, Extension of the direct optimization algorithm for noisy functions, Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come, December 09-12, 2007, Washington D.C.