INTRODUCTION
Optimization in operations research and the management sciences is generally identified with mathematical programming techniques, where analytical expressions for quantities of interest are available. The context of simulation optimization is a stochastic setting that defies analytical tractability, necessitating the use of simulation for estimating (through statistical sampling) system performance measures. The usual generic form of the problem is given as
where θ is the controllable set of parameters and Θ defines the constraint set on θ. We will assume throughout that the objective function is an expectation, that is,
with ω representing a sample path (or simulation replication) and L(θ, ω) the corresponding sample performance estimate. Objective functions of other forms (e.g., quantiles) can also be handled in a similar manner, and of course probabilities are simply expectations of indicator functions. In contrast to mathematical programming objective functions,...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andradóttir, S. (1998). “Simulation Optimization,” Chapter 9 in Handbook of Simulation, ed. J. Banks, Wiley, New York.
Barton, R.R. and Ivey, J.S. (1996). “Nelder-Mead Simplex Modifications for Simulation Optimization,” Management Science, 42, 954–973.
Bertsekas, D.P. and Tsitsiklis, J.N. (1996). Neuro-Dynamic Programming, Athena Scientific, Cambridge, Massachusetts.
Fu, M.C. (1994). “Optimization via Simulation: A Review,” Annals Operations Research, 53, 199–248.
Fu, M.C. and Hill, S.D. (1997). “Optimization of Simulation of discrete-event stochastic systems Discrete Event Systems via Simultaneous Perturbation Stochastic Approximation,” IIE Transactions, 29, 233–243.
Fu, M.C. and Hu, J.Q. (1997). Conditional Monte Carlo: Gradient Estimation and Optimization Applications, Kluwer Academic, Dordrecht.
Glasserman, P. (1991). Gradient Estimation Via Perturbation Analysis, Kluwer Academic, Dordrecht.
Ho, Y.C. and Cao, X.R. (1991). Perturbation Analysis of Discrete Event Dynamic Systems, Kluwer Academic, Dordrecht.
Ho, Y.C., Sreenevas, R., and Vakili, P. (1992). “Ordinal Optimization of DEDS,” Discrete-Event Dynamic Systems: Theory and Applications, 2, 61–88.
Hochberg, Y. and Tamhane, A.C. (1987). Multiple Comparison Procedures, Wiley, New York.
Jacobson, S.H. and Schruben, L.W. (1989). “A Review of Techniques for Simulation Optimization,” Operations Research Letters, 8, 1–9.
Kleijnen, J.P.C. (1987). Statistical Tools for Simulation Practitioners, Marcel Dekker, New York.
Kushner, H.J., and Yin, G.G. (1997). Stochastic Approximation Algorithms and Applications, Springer, Berlin.
Law, A.M. and Kelton, W.D. (1991). Simulation Modeling and Analysis, 2nd edition, McGraw-Hill, New York.
Pflug, G.C. (1996). Optimization of Stochastic Models: The Interface Between Simulation and Optimization, Kluwer Academic, Dordrecht.
Plambeck, E.L., Fu, B.-R., Robinson, S.M., and Suri, R. (1996). “Sample Path Optimization of Convex Stochastic Performance Functions,” Mathematical Programming, 75, 137–176.
Rubinstein, R.Y. and Shapiro, A. (1993). Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method, Wiley, New York.
Safizadeh, M.H. (1990). “Optimization in Simulation: Current Issues and the Future Outlook,” Naval Research Logistics, 37, 807–825.
Sargent, R.G. (1991). “Research Issues in Metamodeling,” Proceedings of the Winter Simulation Conference, 888–893.
Spall, J.C. (1992). “Multivariate Stochastic Approximation Using a Simultaneous Perturbation Gradient Approximation,” IEEE Transactions on Automatic Control, 37, 332–341.
Yang, W.N. and Nelson, B.L. (1991). “Using Common Random Numbers and Control Variates in Multiple-Comparison Procedures,” Operations Research, 39, 583–591.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Kluwer Academic Publishers
About this entry
Cite this entry
Fu, M.C. (2001). SIMULATION Optimization . In: Gass, S.I., Harris, C.M. (eds) Encyclopedia of Operations Research and Management Science. Springer, New York, NY. https://doi.org/10.1007/1-4020-0611-X_958
Download citation
DOI: https://doi.org/10.1007/1-4020-0611-X_958
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-7923-7827-3
Online ISBN: 978-1-4020-0611-1
eBook Packages: Springer Book Archive