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Statistical tools for simulation design and analysis I: finding important independent variables through screening designs: a comparison of methods

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Linda C. MaloneAuthors Info & Claims

WSC '00: Proceedings of the 32nd conference on Winter simulation

Pages 749 - 754

Published: 10 December 2000 Publication History

Abstract

Once a simulation model is developed, designed experiments may be employed to efficiently optimize the system. Designed experiments are used on "real" production systems as well. The first step is to screen for important independent variables. Several screening methods are compared and contrasted in terms of efficiency, effectiveness, and robustness. These screening methods range from the classical factorial designs and two-stage group screening to new, more novel designs including sequential bifurcation (SB) and iterated fractional factorial designs (IFFD). Conditions for the use of the methods are provided along with references on how to use them.

References

[1]

Andres, T. H. 1997. Sampling methods and sensitivity analysis for large parameter sets. Journal of Statistical Computation and Simulation. 57(1-4): 77-110.

[2]

Andres, Terry H. and Wayne C. Hajas. 1993. Using iterated fractional factorial design to screen parameters in sensitivity analysis of a probabilistic risk assessment model. Joint International Conference on Mathematical Methods and Supercomputing in Nuclear Applications. ed. Kusters, Stein, and Werner. 2:328-337.

[3]

Bettonvil, Bert and Jack P. C. Kleijnen. 1996. Searching for important factors in simulation models with many factors: sequential bifurcation. European Journal of Operational Research. 96:180-194.

[4]

Campolongo, F., J. Kleijnen, and T. Andres. 2000. Screening methods in sensitivity analysis. In Mathematical and statistical methods for sensitivity analysis of model output. ed. A. Saltelli and K. Chan. Chichester, England: Wiley.

[5]

Christodoulou, C. and M. Georgiopoulos. 2000. Applications of neural networks in electromagnetics. Artech House, in print.

Digital Library

[6]

Elster, Clemens and Arnold Neumaier. 1995. Screening by conference designs. Biometrika. 82(3):589-602.

[7]

Ivanova, Theodora, Linda Malone, and Mansooreh Mollaghasemi. 1999. Comparison of a two-stage group-screening design to a standard 2k-p design for a whole-line semiconductor manufacturing simulation model. In Proceedings of the Winter Simulation Conference v. 1 ed. P. A. Farringtom, H. B. Nembhard, D. T. Sturrock and G. W. Evans, 640-646.

Digital Library

[8]

Jacoby, J. E. and S. Harrison. 1962. Multi-variable experimentation and simulation models. Naval Research Logistics Quarterly. 9:121-136.

[9]

Kleijnen, Jack P. C. 1987. Statistical tools for simulation practitioners. New York: Marcel Dekker.

Digital Library

[10]

Kleijnen, Jack P. C. 1995. Sensitivity analysis and optimization in simulation: design of experiments and case studies. In Proceedings of the Winter Simulation Conference, ed. C. Alexopoulos, K. Kang, W. R. Lilegdon, and D. Goldsman, 133-140.

Digital Library

[11]

Mauro, Carl A. 1984. On the performance of two-stage group screening experiments. Technometrics. 26(3):255-264.

[12]

Mauro, Carl C., and Dennis E. Smith. 1984. Factor screening in simulation: evaluation of two strategies based on random balance sampling. Management science. 30(2):209-221.

[13]

Myers, Raymond H. and Douglas C. Montgomery. 1995. Response surface methodology: process and product optimization using designed experiments. New York: John Wiley & Sons.

Digital Library

[14]

Ratkowsky, David A. 1990. Handbook of nonlinear regression models. New York: Marcel-Dekker.

[15]

Saltelli, A., T. H. Andres, and T. Homma. 1993. Some new techniques in sensitivity analysis of model output. Computational statistics & data analysis. 15:211-27.

Digital Library

[16]

Saltelli, A., T. H. Andres, and T. Homma. 1995. Sensitivity analysis of model output: performance of iterated fractional factorial design (IFFD) method. Computational statistics & data analysis. 20:387-407.

Digital Library

[17]

Smith, Murray. 1996. Neural networks for statistical modeling. London: International Thomson Computer Press.

Digital Library

[18]

Trocine, Linda. June 20, 2000. Designing screening experiments using modern heuristics: dissertation proposal. Unpublished report, University of Central Florida, Orlando.

[19]

Watson, G. S. 1961. A study of the group screening method. Technometrics. 3(3):371-388.

Cited By

Wang WWan HBuckley SMiller J(2014)Sequential procedures for multiple responses factor screeningProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2693950(745-756)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2693950
Chang KLi MWan H(2011)Combining strong and screening designs for large-scale simulation optimizationProceedings of the Winter Simulation Conference10.5555/2431518.2432011(4127-4138)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2432011
Singh ALilja DElçi AMakarevich OOrgun MChefranov APieprzyk JBryukhomitsky YÖrs S(2009)Improving risk assessment methodologyProceedings of the 2nd international conference on Security of information and networks10.1145/1626195.1626205(21-29)Online publication date: 6-Oct-2009
https://dl.acm.org/doi/10.1145/1626195.1626205
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Statistical tools for simulation design and analysis I: finding important independent variables through screening designs: a comparison of methods
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cover image ACM Conferences

WSC '00: Proceedings of the 32nd conference on Winter simulation

December 2000

2014 pages

ISBN:0780365828

Sponsors

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

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Society for Computer Simulation International

San Diego, CA, United States

Publication History

Published: 10 December 2000

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WSC00: Winter Simulation Conference

December 10 - 13, 2000

Florida, Orlando

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Cited By

Wang WWan HBuckley SMiller J(2014)Sequential procedures for multiple responses factor screeningProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2693950(745-756)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2693950
Chang KLi MWan H(2011)Combining strong and screening designs for large-scale simulation optimizationProceedings of the Winter Simulation Conference10.5555/2431518.2432011(4127-4138)Online publication date: 11-Dec-2011
https://dl.acm.org/doi/10.5555/2431518.2432011
Singh ALilja DElçi AMakarevich OOrgun MChefranov APieprzyk JBryukhomitsky YÖrs S(2009)Improving risk assessment methodologyProceedings of the 2nd international conference on Security of information and networks10.1145/1626195.1626205(21-29)Online publication date: 6-Oct-2009
https://dl.acm.org/doi/10.1145/1626195.1626205
Balestrassi PPopova EPaiva AMarangon Lima J(2009)Design of experiments on neural network's training for nonlinear time series forecastingNeurocomputing10.1016/j.neucom.2008.02.00272:4-6(1160-1178)Online publication date: 1-Jan-2009
https://dl.acm.org/doi/10.1016/j.neucom.2008.02.002
Yaesoubi RRoberts STew J(2007)Important factors in screening for colorectal cancerProceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come10.5555/1351542.1351803(1475-1482)Online publication date: 9-Dec-2007
https://dl.acm.org/doi/10.5555/1351542.1351803
Shen HWan HNicol DFujimoto R(2006)A hybrid method for simulation factor screeningProceedings of the 38th conference on Winter simulation10.5555/1218112.1218186(382-389)Online publication date: 3-Dec-2006
https://dl.acm.org/doi/10.5555/1218112.1218186
Shen HWan H(2005)Controlled sequential factorial design for simulation factor screeningProceedings of the 37th conference on Winter simulation10.5555/1162708.1162791(467-474)Online publication date: 4-Dec-2005
https://dl.acm.org/doi/10.5555/1162708.1162791
Barton R(2004)Designing simulation experimentsProceedings of the 36th conference on Winter simulation10.5555/1161734.1161754(73-79)Online publication date: 5-Dec-2004
https://dl.acm.org/doi/10.5555/1161734.1161754
Wan HAnkenman BNelson BFerrin DMorrice D(2003)Issues on simulation and optimization IProceedings of the 35th conference on Winter simulation: driving innovation10.5555/1030818.1030895(565-573)Online publication date: 7-Dec-2003
https://dl.acm.org/doi/10.5555/1030818.1030895

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