Experimental design for simulation: experimental design for simulation

ABSTRACT

This tutorial introduces some of the ideas, issues, challenges, solutions, and opportunities in deciding how to experiment with simulation models to learn about their behavior. Careful planning, or designing, of simulation experiments is generally a great help, saving time and effort by providing efficient ways to estimate the effects of changes in the model's inputs on its outputs. Traditional experimental-design methods are discussed in the context of simulation experiments, as are the broader questions pertaining to planning computer-simulation experiments.

REFERENCES

• 1: Sigrún Andradóttir, A review of simulation optimization techniques, Proceedings of the 30th conference on Winter simulation, p.151-158, December 13-16, 1998, Washington, D.C., United States
• 2: Banks, J., J. S. Carson, B. L. Nelson, and D. M. Nicol. 2001. Discrete-event system simulation. 3rd ed. Upper Saddle River, N.J.: Prentice-Hall.
• 3: Russell R. Barton, Simulation metamodels, Proceedings of the 30th conference on Winter simulation, p.167-176, December 13-16, 1998, Washington, D.C., United States
• 4: Barton, R. R. 1999. Graphical methods for the design of experiments. New York: Springer-Verlag.
• 5: Russell R. Barton, Simulation experiments: designing simulation experiments, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
• 6: George E P Box , Norman R Draper, Empirical model-building and response surface, John Wiley & Sons, Inc., New York, NY, 1986
• 7: Box, G.E.P., W. G. Hunter, and J. S. Hunter. 1978. Statistics for experimenters: an introduction to design, data analysis, and model building. New York: John Wiley.
• 8: Michael C. Fu, Feature Article: Optimization for simulation: Theory vs. Practice, INFORMS Journal on Computing, v.14 n.3, p.192-215, July 2002 [doi>10.1287/ijoc.14.3.192.113]
• 9: Fred Glover , James P. Kelly , Manuel Laguna, New advances for wedding optimization and simulation, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.255-260, December 05-08, 1999, Phoenix, Arizona, United States [doi>10.1145/324138.324223]
• 10: Sarah J. Hood , Peter D. Welch, Experimental design issues in simulation with examples from semiconductor manufacturing, Proceedings of the 24th conference on Winter simulation, p.255-263, December 13-16, 1992, Arlington, Virginia, United States [doi>10.1145/167293.167346]
• 11: Sarah J. Hood , Peter D. Welch, Response surface methodology and its application in simulation, Proceedings of the 25th conference on Winter simulation, p.115-122, December 12-15, 1993, Los Angeles, California, United States [doi>10.1145/256563.256598]
• 12: W. David Kelton, Statistical analysis of simulation output, Proceedings of the 29th conference on Winter simulation, p.23-30, December 07-10, 1997, Atlanta, Georgia, United States [doi>10.1145/268437.268443]
• 13: W. David Kelton, Design of experiments: experimental design for simulation, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
• 14: W. David Kelton , Randall P. Sadowski , Deborah A. Sadowski, Simulation with Arena, McGraw-Hill, Inc., New York, NY, 2002
• 15: Kleijnen, J.P.C. 1998. Experimental design for sensitivity analysis, optimization, and validation of simulation models. In Handbook of simulation, ed. J. Banks, 173--223. New York: John Wiley.
• 16: Averill M. Law , David M. Kelton, Simulation Modeling and Analysis, McGraw-Hill Higher Education, 1999
• 17: Montgomery, D. C. 1997. Design and analysis of experiments. 4th ed. New York: John Wiley.
• 18: James J. Swain , Phillip A. Farrington, Designing simulation experiments for evaluating manufacturing systems, Proceedings of the 26th conference on Winter simulation, p.69-76, December 11-14, 1994, Orlando, Florida, United States