A comprehensive literature classification of simulation optimisation methods
Simulation Optimization (SO) provides a structured approach to the system design and configuration when analytical expressions for input/output relationships are unavailable. Several excellent surveys have been written on this topic. Each survey concentrates on only few classification criteria. This paper presents a literature survey with all classification criteria on techniques for SO according to the problem of characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). The survey focuses specifically on the SO problem that involves single per-formance measure
|Date of creation:||24 May 2010|
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- Russell R. Barton & John S. Ivey, Jr., 1996. "Nelder-Mead Simplex Modifications for Simulation Optimization," Management Science, INFORMS, vol. 42(7), pages 954-973, July.
- Marvin K. Nakayama & Perwez Shahabuddin, 1998. "Likelihood Ratio Derivative Estimation for Finite-Time Performance Measures in Generalized Semi-Markov Processes," Management Science, INFORMS, vol. 44(10), pages 1426-1441, October.
- Kleijnen, Jack P. C., 2005.
"An overview of the design and analysis of simulation experiments for sensitivity analysis,"
European Journal of Operational Research,
Elsevier, vol. 164(2), pages 287-300, July.
- Kleijnen, J.P.C., 2004. "An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis," Discussion Paper 2004-16, Tilburg University, Center for Economic Research.
- Azadivar, Farhad & Lee, Young-Hae, 1988. "Optimization of discrete variable stochastic systems by computer simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 30(4), pages 331-345.
- Hunt, F.Y., 2005. "Sample path optimality for a Markov optimization problem," Stochastic Processes and their Applications, Elsevier, vol. 115(5), pages 769-779, May.
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