Efficient identification of important factors in large scale simulations

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Efficient identification of important factors in large scale simulations

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Winter Simulation Conference archive
Proceedings of the 18th conference on Winter simulation table of contents

Washington, D.C., United States

Pages: 296 - 305

Year of Publication: 1986

ISBN:0-911801-11-1

Author

Carl A. Mauro

Desmatics, Inc., P.O. Box 618, State College, PA

Sponsor

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 4, Downloads (12 Months): 16, Citation Count: 1

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ABSTRACT

Large, complex computer simulation models can require prohibitively costly and time-consuming experimental programs to study their behavior. Therefore we may want to concentrate the analysis on the set of “most important” factors (i.e., input variables). Factor screening experiments, which attempt to identify the more important variables, can be extremely useful in the study of such models. The number of computer runs available for screening, however, is usually severely limited. In fact, the number of factors often exceeds the number of available runs. In this paper we present a survey of supersaturated designs for use in factor screening experiments. The designs considered are: random balance, systematic supersaturated, group screening, modified group screening, T-optimal, R-optimal, and search designs. We discuss in general terms the basic technique, advantages, and disadvantages of each procedure surveyed.

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.

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