Factor screening in simulation experiments: Review of sequential bifurcation

International Series in Operations Research and Management Science

Volumn 133, Issue , 2009, Pages 153-167

  Kleijnen, Jack P C ^a  

^a TILBURG UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84885919270     PISSN: 08848289     EISSN: None     Source Type: Book Series    
DOI: 10.1007/b110059_8     Document Type: Chapter

References (46)

1. Allen, T. T., and M. Bernshteyn. 2003. Supersaturated designs that maximize the probability of finding the active factors. Technometrics 45(1):1–8.
2. Andres, T. H. 1997. Sampling methods and sensitivity analysis for large parameter sets. Journal of Statistical Computation and Simulation 57(1/4):77–110.
3. Andres, T. H., and W. C. Hajas. 1993. Using iterated fractional factorial design to screen parameters in sensitivity analysis of a probabilistic risk assessment model. In Proceedings of the Joint International Conference on Mathematical Methods and Supercomputing in Nuclear Applications, Karlsruhe, Germany, April 19–23, 2:328–337.
4. Ankenman, B. E., R. C. H. Cheng, and S. M. Lewis. 2006. A polytope method for estimating factor main effects efficiently. In Proceedings of the 2006 Winter Simulation Conference, eds. L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, pp. 369–375. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.
5. Bettonvil, B. 1990. Detection of important factors by sequential bifurcation. Ph.D. dissertation, Tilburg University Press, Tilburg.
6. Bettonvil, B., and J. P. C. Kleijnen. 1996. Searching for important factors in simulation models with many factors: Sequential bifurcation. European Journal of Operational Research 96:180–194.
7. Campolongo, F., J. Cariboni, and A. Saltelli. 2007. An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software 22(10):1509–1518.
8. Campolongo, F., J. P. C. Kleijnen, and T. Andres. 2000. Screening methods. In Sensitivity Analysis, eds. A. Saltelli, K. Chan, and E. M. Scott, pp. 65–89. Chichester, England: Wiley.
9. Cheng, R. C. H. 1997. Searching for important factors: Sequential bifurcation under uncertainty. In Proceedings of the 1997 Winter Simulation Conference, eds. S. Andrad´ottir, K. J. Healy, D. H. Withers, and B. L. Nelson, pp. 275–280. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.
10. Cheng, R. C. H., and W. Holland. 1999. Stochastic sequential bifurcation: Practical issues. In Proceedings of UK Simulation S ociety Conference, eds. D. Al-Dabass and R. C. H. Cheng, pp. 74–80. Nottingham: UKSIM.
11. Chipman, H. 2006. Prior distributions for Bayesian analysis of screening experiments. In Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics, eds. A. Dean and S. Lewis, pp. 235–267. New York: Springer-Verlag.
12. De Vos, C., H.W. Saatkamp, M. Nielen, and R. B.M. Huirne. 2006. Sensitivity analysis to evaluate the impact of uncertain factors in a scenario tree model for classical swine fever introduction. Risk Analysis 26(5):1311–1322.
13. Elster, C., and A. Neumaier. 1995. Screening by conference designs. Biometrika 82(3):589–602.
14. Georgiou, S. D. 2007. New two-variable full orthogonal designs and related experiments with linear regression models. Statistics & Probability Letters 77(1):25–31.
15. Gilmour, S. G. 2006. Factor screening via supersaturated designs. In Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics, eds. A. Dean and S. Lewis, pp. 169–190. New York: Springer-Verlag.
16. Kleijnen, J. P. C. 2008. Design and Analysis of Simulation Experiments. New York: Springer Science + Business Media.
17. Kleijnen, J. P. C., B. Bettonvil, and F. Persson. 2006. Screening for the important factors in large discrete-event simulation: sequential bifurcation and its applications. In: Screening Methods for Experimentation in Industry, Drug Discovery, and Genetics, eds. A. Dean and S. Lewis, pp. 287–307. New York: Springer-Verlag.
18. Kleijnen, J. P. C., S. M. Sanchez, T. W. Lucas, and T. M. Cioppa. 2005. State-of-the-art review: A user’s guide to the brave new world of designing simulation experiments. INFORMS Journal on Computing 17(3):263–289.
19. Kleijnen, J. P. C., and W. C. M. van Beers. 2004. Application-driven sequential designs for simulation experiments: Kriging metamodeling. Journal of the Operational Research Society 55(9):876–883.
20. Kleijnen, J. P. C., G. van Ham, and J. Rotmans. 1992. Techniques for sensitivity analysis of simulation models: A case study of the CO2 greenhouse effect. Simulation 58(6):410–417.
21. Lewis, S. M., and A. M. Dean. 2001. Detection of interactions in experiments on large numbers of factors (including discussion). Journal Royal Statistical Society B 63:633–672.
22. Li, L., andW. Li. 2005. Tabu search and perturbation methods in the construction of supersaturated designs. American Journal of Mathematical and Management Sciences 25(12):189–205.
23. Lim, E., and P. W. Glynn. 2006. Simulation-based response surface computation in the presence of monotonicity. In Proceedings of the 2006 Winter Simulation Conference, eds. L. F. Perrone, F. P.Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R.M. Fujimoto, pp. 264–271. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.
24. Miller, G. A. 1956. The magical number seven plus or minus two: Some limits on our capacity for processing information. The Psychological Review 63:81–97.
25. Morris, M. D. 2006. An overview of group factor screening. In Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics, eds. A. Dean and S. Lewis, pp. 191–206. New York: Springer-Verlag.
26. Myers, R. H., and D. C. Montgomery. 2002. Response Surface methodology: Process and Product Optimization Using Designed Experiments, 2nd edition. New York: Wiley.
27. Park, S., J. W. Fowler, G. T. Mackulak, J. B. Keats, and W. M. Carlyle. 2002. D-optimal sequential experiments for generating a simulation-based cycle time-throughput curve. Operations Research 50(6):981–990.
28. Persson, J. F., and J. Olhager. 2002. Performance simulation of supply chain designs. International Journal of Production Economics 77:231–245.
29. Saltelli, A., M. Ratto, S. Tarantola, and F. Campolongo. 2005. Sensitivity analysis of chemical models. Chemical Reviews 105(7):2811–2827.
30. Saltelli, A., S. Tarantola, F. Campolongo, and M. Ratto. 2004. Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Chichester, England: Wiley.
31. Sanchez, S. M., F. Moeeni, and P. J. Sanchez. 2006. So many factors, so little time. . . Simulation experiments in the frequency domain. International Journal of Production Economics 103(1):149–165.
32. Schonlau, M., and W. J. Welch. 2006. Screening the input factors to a computer code via Analysis of Variance and visualization. In Screening: Methods for Experimentation in Industry, Drug Discovery, and Genetics, eds. A. Dean and S. Lewis, pp. 308–327. New York: Springer-Verlag.
33. Schruben, L. W., and V. J. Cogliano. 1987. An experimental procedure for simulation response surface model identification. Communications of the ACM 30(8):716–730.
34. Shen, H., and H. Wan. 2006. A hybrid method for simulation factor screening. In Proceedings of the 2006 Winter Simulation Conference, eds. L. F. Perrone, F. P.Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, pp. 382–389. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.
35. Simpson, T. W., A. J. Booker, D. Ghosh, A. A. Giunta, P. N. Koch, and R.-J. Yang. 2004. Approximation methods in multidisciplinary analysis and optimization: a panel discussion. Structural and Multidisciplinary Optimization 27(5):302–313.
36. Tsai, P.-W., S. G. Gilmour, and R. Mead. 2007. Three-level main-effects designs exploiting prior information about model uncertainty. Journal of Statistical Planning and Inference 137(2):619–627.
37. Tu, J., and D. R. Jones. 2003. Variable screening in metamodel design by cross-validated Moving Least Squares method. In Proceedings of the 44th AIAA ASME ASCE AHS ASC Structures, Structural Dynamics, and Materials Conference, AIAA-2003-1669, Norfolk, Virginia, April 7–10, 2003.
38. Van Beers, W. C. M., and J. P. C. Kleijnen. 2008. Customized sequential designs for random simulation experiments: Kriging metamodeling and bootstrapping. European Journal of Operational Research 186(3):1099–1113.
39. Wan, H., B. E. Ankenman, and B. L. Nelson. 2006a. Controlled sequential bifurcation: A new factor-screening method for discrete-event simulation. Operations Research 54(4):743–755.
40. Wan, H., B. E. Ankenman, and B. L. Nelson. 2006b. Simulation factor screening with controlled sequential bifurcation in the presence of interactions. Working Paper, Purdue University, West Lafayette, Indiana.
41. Wu, C. F. J., and M. Hamada. 2000. Experiments: Planning, Analysis, and Parameter Design Optimization. New York: Wiley.
42. Xu, J., F. Yang, and H. Wan. 2007. Controlled sequential bifurcation for software reliability study. In Proceedings of the 2007 Winter Simulation Conference, eds. S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, pp. 281–288. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.
43. Yamada, S., M. Matsui, T. Matsui, D. K. J. Lin, and T. Takahashi. 2006. A general construction method for mixed-level supersaturated design Computational Statistics & Data Analysis 50(1):254–265.
44. Yu, H.-F. 2007. Designing a screening experiment with a reciprocalWeibull degradation rate. Computers & Industrial Engineering 52(2):175–191.
45. Zeigler, B. P., H. Praehofer, and T. G. Kim. 2000. Theory of Modeling and Simulation, 2nd edition. San Diego: Academic Press.
46. Zhang, Q.-Z., R.-C. Zhang, and M.-Q. Liu. 2007. A method for screening active effects in supersaturated designs. Journal of Statistical Planning and Inference 137(6):2068–2079.