Simulation optimization: simulation optimization

ABSTRACT

Simulation optimization has received considerable attention from both simulation researchers and practitioners. In this tutorial we present a broad introduction to simulation optimization and the many techniques that have been suggested to solve simulation optimization problems. Both continuous and discrete problems are discussed, but an emphasis is placed on discrete problems and practical methods for addressing such problems.

REFERENCES

• 1: Theodore Allen , Liyang Yu, Low cost response surface methods for and from simulation optimization, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
• 2: Alrefaei, M. H. and S. Andradóttir. 1998. "A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization," technical report, Department of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA.
• 3: 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
• 4: Jay April , Fred Glover , James Kelly , Manuel Laguna, Optimization and system selection: simulation/optimization using "real-world" applications, Proceedings of the 33nd conference on Winter simulation, December 09-12, 2001, Arlington, Virginia
• 5: Farhad Azadivar, Simulation optimization methodologies, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.93-100, December 05-08, 1999, Phoenix, Arizona, United States [doi>10.1145/324138.324168]
• 6: Bitron, J. 2000. "Optimizing AutoMod Models with Auto-Stat," AutoFlash, February 2000.
• 7: Stephen E. Chick , Koichiro Inoue, A decision-theoretic approach to screening and selection with common random numbers, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.603-610, December 05-08, 1999, Phoenix, Arizona, United States [doi>10.1145/324138.324444]
• 8: Stephen E. Chick , Koichiro Inoue, New results on procedures that select the best system using CRN, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
• 9: Dudewicz, E. J. and S. R. Dalal. 1975. "Allocation of Observations in Ranking and Selection with Unequal Variances," The Indian Journal of Statistics, 37B, 28--78.
• 10: 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]
• 11: Michael C. Fu , Sigrún Andradóttir , John S. Carson , Fred Glover , Charles R. Harrell , Yu-Chi Ho , James P. Kelly , Stephen M. Robinson, Integrating optimization and simulation: research and practice, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
• 12: Glasserman, P. 1991. Gradient Estimation via Perturbation Analysis, Kluwer Academic Publishers, Boston, MA.
• 13: 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]
• 14: Fred Glover , Fred Laguna, Tabu Search, Kluwer Academic Publishers, Norwell, MA, 1997
• 15: Gül Gürkan , A. Yonca Özge , Stephen M. Robinson, Sample-path optimization in simulation, Proceedings of the 26th conference on Winter simulation, p.247-254, December 11-14, 1994, Orlando, Florida, United States
• 16: Jorge Haddock , John Mittenthal, Simulation optimization using simulated annealing, Computers and Industrial Engineering, v.22 n.4, p.387-395, Oct. 1992 [doi>10.1016/0360-8352(92)90014-B]
• 17: Charles R. Harrell , Rochelle N. Price, Simulation modeling and optimization using ProModel, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
• 18: Ho, Y. C. and X. R. Cao. 1991. Perturbation Analysis of Discrete Event Dynamic Systems, Kluwer Academic Publisher, Norwell, MA.
• 19: Ho, Y. C., C. G. Cassandras, C. H. Chen, and L. Y. Dai. 2000. "Ordinal Optimization and Simulation," in Journal of Operational Research Society51, 490--500.
• 20: Ho, Y. C., R. Screenivas, and P. Vakili. 1992. "Ordinal Optimization of Discrete Event Dynamic Systems," Discrete Event Dynamic Systems, 2, 61--88.
• 21: Kiefer, J. and J. Wolfowitz. 1952. "Stochastic Estimation of the Maximum of a Regression Function," Annals of Mathematical Statistics, 23, 462--466.
• 22: Matejcik, F. J. and B. L. Nelson. 1995. "Two-Stage Multiple Comparisons with the Best for Computer Simulation," Operations Research, 43, 633--640.
• 23: Sigurdur Ólafsson, Iterative ranking-and-selection for large-scale optimization, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.479-485, December 05-08, 1999, Phoenix, Arizona, United States [doi>10.1145/324138.324302]
• 24: Sigurdur Ólafsson , Jumi Kim, Simulation optimization: towards a framework for black-box simulation optimization, Proceedings of the 33nd conference on Winter simulation, December 09-12, 2001, Arlington, Virginia
• 25: Rinott, Y. 1978. "On Two-Stage Selection Procedures and Related Probability-in-Equalities," Communications in Statistics, A7, 799--811.
• 26: Robbins, H. and S. Monro. 1951. "A Stochastic Approximation Method," Annals of Mathematical Statistics, 22, 400--407.
• 27: Stephen M. Robinson, Analysis of sample-path optimization, Mathematics of Operations Research, v.21 n.3, p.513-528, Aug. 1996 [doi>10.1287/moor.21.3.513]
• 28: R. Rubinstein, How to optimize discrete-event systems from a single sample path by the score function method, Annals of Operations Research, v.27 n.1-4, p.175-212, Dec. 1990 [doi>10.1007/BF02055195]
• 29: Rubinstein, R. Y. and A. Shapiro. 1993. Discrete Event Systems: Sensitivity Analysis and Stochastic Approximation using the Score Function Method, John Wiley & Sons, New York.
• 30: Leyuan Shi , Sigurdur Ólafsson, An integrated framework for deterministic and stochastic optimization, Proceedings of the 29th conference on Winter simulation, p.358-365, December 07-10, 1997, Atlanta, Georgia, United States [doi>10.1145/268437.268508]
• 31: James R. Swisher , Paul D. Hyden , Sheldon H. Jacobson , Lee W. Schruben, Simulation optimization: a survey of simulation optimization techniques and procedures, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida