A frequency domain metamodeling approach to transient sensitivity analysis

IIE Transactions (Institute of Industrial Engineers)

Volumn 33, Issue 3, 2001, Pages 229-244

  Morrice, Douglas J ^a   Schruben, Lee W ^b  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b DEPARTMENT OF INDUSTRIAL ENGINEERING AND OPERATIONS RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; FREQUENCY DOMAIN ANALYSIS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; RANDOM PROCESSES;

FREQUENCY DOMAIN METHODOLOGY (FDM); TRANSIENT SENSITIVITY ANALYSIS;

SENSITIVITY ANALYSIS;

EID: 0035276142     PISSN: 0740817X     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007607717169     Document Type: Article

References (33)

1. Anderson, T.W. (1971) The Statistical Analysis of Time Series, John Wiley and Sons, New York.
2. Anon (1990) SAS/STAT User's Guide, Version 6, SAS Institute, Inc., Cary, NC.
3. Barton, R.R. (1994) Metamodeling: a state of the art review, in 1994 Winter Simulation Conference Proceedings, Tew, J.D., Manivannan, S., Sadowski, D.A. and Seila, A.F. (eds), The Institute of Electrical and Electronics Engineers, Piscataway, NJ, pp. 237-244.
4. Brockwell, P.J. and Davis, R.A. (1987) Time Scries: Theory and Methods, Springer-Verlag, New York.
5. Daley, D.J. (1967) The serial correlation coefficients of waiting times in a stationary single server queue. Journal of the Australian Mathematical Society, 8, 683-699.
6. Draper, N.R. and Smith, H. (1981) Applied Regression Analysis, 2nd edn, John Wiley and Sons, New York.
7. Duncan, D.B. and Jones, R.H. (1966) Multiple regression with stationary errors. Journal of the American Statistical Association, 6, 917-928.
8. Eick, S.G., Massey, W.A. and Whitt, W. (1987) M,/G/oo queues with sinusoidal arrival rates. Management Science, 39, 241-252.
9. Eick, S.G., Massey, W.A. and Whitt, W. (1993) Infinite-server approximations for multi-server loss models with time-dependent arrival rates. Working Paper, AT&T Bell Laboratories, Middletown, NJ.
10. Engle, R.F. (1974) Band spectrum regression. International Economic Review, 15, 1-11.
11. Engle, R.F. and Gardner, R. (1976) Some finite sample properties of spectral estimators for linear regression. Econometrica, 44,149-165.
12. Gross, D. and Harris, C.M. (1985) Fundamentals of Queueing Theory, 2nd edn, John Wiley and Sons, New York.
13. Hannan, E.J. (1963) Regression for time series, in Time Series Analysis, Rosenblatt, M. (ed), John Wiley and Sons, New York, pp. 17-37.
14. Hazra, M., Morrice, D.J. and Park, S.K. (1997) A simulation clockbased solution to the frequency domain experiment indexing problem. IIE Transactions, 29(9), 769-782.
15. Hazra, M. and Park, S.K. (1993) An approximate expression for the frequency domain response of an M/M/\ queue. Technical Report WM-93-3, Department of Computer Science, College of William and Mary, Williamsburg, VA.
16. Heidelberger, P. and Welch, P. (1981) A spectral method for confidence interval generation and run length control in simulations. Communications of the Association for Computing Machinery, 24, 233-245.
17. Jacobson, S.H. (1993) Variance and bias reduction techniques for the harmonic gradient estimator. Applied Mathematics and Computation: Modeling of the Environment, 55, 153-186.
18. Jacobson, S.H., Buss, A.H. and Schruben, L.W. (1991) Driving frequency selection for frequency domain simulation experiments. Operations Research, 39, 917-924.
19. Judge, G.G., Griffiths, W.E., Hill, R.C., Lutkepohl, H. and Lee, T.C. (1985) The Theory and Practice of Econometrics, 2nd edn, John Wiley and Sons, New York.
20. Kleijnen, J.P.C. (1987) Statistical Tools for Simulation Practitioners, Marcel Dekker, New York.
21. Law, A.M. and Kelton, W.D. (1991). Simulation Modeling and Analysis, 2nd edn, McGraw-Hill, New York.
22. Miller, R.G. (1981) Simultaneous Statistical Inference, 2nd edn, Springer-Verlag, New York.
23. Morrice, D.J. (1995) An experimental approach to transient sensitivity analysis. Working Paper 94/95-3-7, Department of Management Science and Information Systems, The University of Texas at Austin, Austin, TX.
24. Morrice, D.J. and Bardhan, I.R. (1995) A weighted least squares approach to computer simulation factor screening. Operations Research, 43, 792-806.
25. Morrice, D.J. and Gupta, A. (1994) Transient sensitivity analysis in computer simulation, in 1994 European Simulation Miilticonfercnce Proceedings, Guasch, A. and Huber, R. (eds), Society for Computer Simulation International, Barcelona, Spain, pp. 858-862.
26. Morrice, D.J. and Schruben, L.W. (1993) Simulation factor screening using harmonic analysis. Management Science, 39, 1459-1476.
27. Priestley, M.B. (1981) Spectral Analysis and Times Series, Academic Press, London, England.
28. Sanchez, P.J. and Sanchez, S.M. (1991) Design of frequency-domain experiments for discrete-valued factors. Applied Mathematics and Compulation: Modeling of the Environment, 42(1), 1-21.
29. Schruben, L.W. and Cogliano, V.J. (1987) An experimental procedure for simulation response surface. Communications of the Association for Computing Machinery, 30, 716-730.
30. Taylor, W.E. (1977) Small sample properties of a class of two stage Aitken estimators. Econometrica, 45, 497-508.
31. Taylor, W.E. (1978) The heteroscedastic linear model: exact finite sample results. Econometrica, 46, 663-675.
32. Watson, G.S. (1967) Linear least squares regression. Annals of Mathematical Statistics, 38, 1679-1699.
33. Welch, P.D. (1983) The statistical analysis of simulation results, in Computer Performance Modeling Handbook, Lavenberg, S.S. (ed), Academic Press, New York, pp. 267-329. Douglas J. Morrice is an Associate Professor in the MSIS Department at The University of Texas at Austin. He received his undergraduate degree in Operations Research at Carleton University in Ottawa, Canada. He holds an M.S. and a Ph.D. in Operations Research and Industrial Engineering from Cornell University. His research interests include operations modeling and the statistical design and analysis of large scale simulation experiments. Dr. Morrice is a member INFORMS. He served as the Secretary for the INFORMS College on Simulation (1994-1996) and was Co-Editor of the Proceedings of the 1996 Winter Simulation Conference. Lee W. Schruben is a Professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. He holds a B.S. degree in Engineering from Cornell, a M.S. degree in Statistics from the University of North Carolina, and a Ph.D. in Administrative Sciences from Yale. His research is on discrete event simulation modeling and analysis methodologies in a variety of applications including manufacturing, agriculture, health care, banking, and broadcasting. Prior to joining the Berkeley faculty in 1999, Professor Schruben was in the School of Operaions Research and Industrial Engineering at Cornell University where he was the Andrew S. Schultz Jr. Professor of Industrial Engineering. He has been the Distinguished Visiting Professor at SEMATCH and the Principal Investigator for Cornell's Center for Semiconductor Manufacturing Research.