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Statistical considerations in simulation on a network of microcomputers
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Source Winter Simulation Conference archive
Proceedings of the 17th conference on Winter simulation table of contents
San Francisco, California, United States
Pages: 388 - 393  
Year of Publication: 1985
ISBN:0-911801-07-3
Authors
William E. Biles  Industrial Engineering Department, Louisiana State University, Baton Rouge, LA
Cheryl M. Daniels  Arthur Young Company, 2121 San Jacinto St., Dallas, TX
Tamilea J. O'Donnell  Rockwell International, 3370 Miraloma Avenue, Anaheim, CA
Sponsor
SIGSIM: ACM Special Interest Group on Simulation and Modeling
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 2,   Downloads (12 Months): 8,   Citation Count: 6
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ABSTRACT

Statistical considerations in simulating complex systems on a hierarchical network of low-cost microcomputers are discussed. Network configurations, allocation of computer tasks to nodes of the network, and assignment of simulation trials are examined. The binary tree and its X-tree variant are found to be especially attractive network configurations in simulation applications with simulation tasks assigned to nodes according to a model-frame/experiment-frame/output-frame trichotomy. Procedures for performing designed simulation experiments, including factor screening experiments, and for conducting variance reduction through manipulation of random number streams are presented.


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
Biles, W.E. and J.J. Swain, Optimization and Industrial Experimentation, John Wiley and Sons, New York, 1980.
 
2
Comfort, J., and A. Miller, "Considerations in the Design of a Multiprocessor-Based Simulation Computer," i q Modeling and Simulation on Microcomputers (L. Leventhal, Editor), Society of Computer Simulation, LaJolla, CA, 1981.
 
3
 
4
Despain, A.M., and D. A. Patterson, "X-Tree: A Tree Structured Multiprocessor Computer Architecture," Computer Science Division, Unive#sity of California, Berkeley, CA, 1981.
 
5
Fedoro~, V.V., Theory of Optimal Expe#iments, Academic Press, New York, 1972, pp 211-220.
 
6
Fernandes, C., "A Computer Simulation Model to Analyze Production Processes in the American and Japanese Automobile Industries," unpublished MSIE thesis,. Industrial Engineering Department, Louisiana State University, Baton Rouge, LA, December 1984.
 
7
Harrison, T.J., "Minis, Micros, and Miscellany," Chapter 8 in Minicomputers in Industrial Control, (T.J. Harrison, Editor), Prentice-Hall, Englewood Cliffs, Nj, 1978.
 
8
Kleijnen, J.P.C., Statistical Techniques in Simulation, Part I, Marcel Dekker, New York, 1974, pp 186-200.
 
9
Kleijnen, J.P.C., Statistical Techniques in Simulation, Part II, Marcel Dekker, New York, 1974, pp 287-407.
 
10
Oren, T.I., and B.F. Ziegler, "Concepts for Advanced Simulation Methodologies," Simulation, March 1979.
 
11
Pegden, C.D., Introduction to SIIiAN, Systems Modeling Corp., State College, PA, I~82.
 
12
Rozenblit, J.W., "Experimental Frames for Distributed Simulation Architectures," Distributed Simulation, Simulation Councils, Inc., LaJolla, CA, January 1985, pp 14-20.
 
13
Schruben, L.W., and B. H. Margolin, "Pseudo-Random Number Assignment in Statistically Designed Simulation and Distribution Sam~lin9 Experiments," Journal of the American Statistical Association, VoI. 73, 1978, pp 504-540.
 
14
Smith, D.E. and C.A. Mauro, "Factor Screening in Computer Simulation," Simulation, February 1982, pp 49-54.
 
15
Wilson, J.R., "Antithetic Sampling with Multivariate Inputs," American Journal of Mathematical and Management Sciences, Vol. 3, No. 2, 1983, pp 121-144.

Collaborative Colleagues:
William E. Biles: colleagues
Cheryl M. Daniels: colleagues
Tamilea J. O'Donnell: colleagues