Finding probably best systems quickly via simulations

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Finding probably best systems quickly via simulations

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ACM Transactions on Modeling and Computer Simulation (TOMACS) archive
Volume 19 , Issue 3 (June 2009) table of contents

Article No. 12

Year of Publication: 2009

ISSN:1049-3301

Author

Takayuki Osogami

IBM Tokyo Research Laboratory, Kanagawa, Japan

Publisher

ACM New York, NY, USA

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appendices and supplements abstract references index terms collaborative colleagues

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APPENDICES and SUPPLEMENTS

Osogami Appendix

Online appendix to finding probably best systems quickly via simulations. The appendix supports the information on article 12.

ABSTRACT

We propose an indifference-zone approach for a ranking and selection problem with the goal of reducing both the number of simulated samples of the performance and the frequency of configuration changes. We prove that with a prespecified high probability, our algorithm finds the best system configuration. Our proof hinges on several ideas, including the use of Anderson's probability bound, that have not been fully investigated for the ranking and selection problem. Numerical experiments show that our algorithm can select the best system configuration using up to 50% fewer simulated samples than existing algorithms without increasing the frequency of configuration changes.

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	Anderson, T. W. 1960. A modification of the sequential probability ratio test to reduce the sample size. Ann. Math. Stat. 31, 1, 165--197.
	2	Bechhofer, R. E., Santner, T. J., and Goldsman, D. M. 1995. Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. John Wiley & Sons, New York.
	3	Justin Boesel , Barry L. Nelson , Seong-Hee Kim, Using Ranking and Selection to "Clean Up" after Simulation Optimization, Operations Research, v.51 n.5, p.814-825, September 2003 [doi>10.1287/opre.51.5.814.16751]
	4	Chun-Hung Chen , Jianwu Lin , Enver Yücesan , Stephen E. Chick, Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization, Discrete Event Dynamic Systems, v.10 n.3, p.251-270, July 2000 [doi>10.1023/A:1008349927281]
	5	Chen, H.-C., Chen, C.-H., and Yücesan, E. 2000b. Computing efforts allocation for ordinal optimization and discrete event simulation. IEEE Trans. Auto. Contr. 45, 5, 960--964.
	6	Stephen E. Chick , Koichiro Inoue, New Procedures to Select the Best Simulated System Using Common Random Numbers, Management Science, v.47 n.8, p.1133-1149, August 2001 [doi>10.1287/mnsc.47.8.1133.10229]
	7	Stephen E. Chick , Koichiro Inoue, New Two-Stage and Sequential Procedures for Selecting the Best Simulated System, Operations Research, v.49 n.5, p.732-743, September 2001 [doi>10.1287/opre.49.5.732.10615]
	8	Dudewicz, E. J. and Dalal, S. R. 1975. Allocation of observations in ranking and selection with unequal variances. Sankhya B37, 1, 28--78.
	9	Fabian, V. 1974. Note on Anderson's sequential procedures with triangular boundary. Annals Stat. 2, 1, 170--176.
	10	Grimmett, G. and Stirzaker, D. 2001. Probability and Random Processes, 3rd ed. Oxford University Press, New York.
	11	Hong, L. J. 2006. Fully sequential indifference-zone selection procedures with variance-dependent sampling. Naval Res. Log. 53, 5, 464--476.
	12	Hong, L. J. and Nelson, B. L. 2005. The tradeoff between sampling and switching: New sequential procedures for indifference-zone selection. IIE Trans. 37, 7, 623--634.
	13	Jennison, C., Johnston, I. M., and Turnbull, B. W. 1982. Asymptotically optimal procedures for sequential adaptive selection of the best of several normal means. In Statistical Decision Theory and Related Topics III. Vol. 2. Academic Press, New York, 55--86.
	14	Seong-Hee Kim , Barry L. Nelson, A fully sequential procedure for indifference-zone selection in simulation, ACM Transactions on Modeling and Computer Simulation (TOMACS), v.11 n.3, p.251-273, July 2001 [doi>10.1145/502109.502111]
	15	Averill M. Law , W. David Kelton, Simulation Modeling and Analysis, McGraw-Hill Higher Education, 1997
	16	Barry L. Nelson , Julie Swann , David Goldsman , Wheyming Song, Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large, Operations Research, v.49 n.6, p.950-963, November 2001 [doi>10.1287/opre.49.6.950.10019]
	17	Takayuki Osogami , Toshinari Itoko, Finding probably better system configurations quickly, Proceedings of the joint international conference on Measurement and modeling of computer systems, June 26-30, 2006, Saint Malo, France
	18	Juta Pichitlamken , Barry L. Nelson, A combined procedure for optimization via simulation, ACM Transactions on Modeling and Computer Simulation (TOMACS), v.13 n.2, p.155-179, April 2003 [doi>10.1145/858481.858485]
	19	Rinott, Y. 1978. On two-stage selection procedures and related probability-inequalities. Communications in Statistics—Theory and Methods A7, 8, 799--811.
	20	Stein, C. 1945. A two-sample test for a linear hypothesis whose power is independent of the variance. Annals Math. Stat. 16, 3, 243--258.
	21	James R. Swisher , Sheldon H. Jacobson , Enver Yücesan, Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey, ACM Transactions on Modeling and Computer Simulation (TOMACS), v.13 n.2, p.134-154, April 2003 [doi>10.1145/858481.858484]