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Simulation-based optimization: practical introduction to simulation optimization
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Source Winter Simulation Conference archive
Proceedings of the 35th conference on Winter simulation: driving innovation table of contents
New Orleans, Louisiana
SESSION: Introductory tutorials table of contents
Pages: 71 - 78  
Year of Publication: 2003
ISBN:0-7803-8132-7
Authors
Jay April  OptTek Systems, Boulder, CO
Fred Glover  OptTek Systems, Boulder, CO
James P. Kelly  OptTek Systems, Boulder, CO
Manuel Laguna  OptTek Systems, Boulder, CO
Sponsors
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
NIST : National Institute of Standards and Technology
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
ACM: Association for Computing Machinery
(SCS) : The Society for Modeling and Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association
Publisher
Winter Simulation Conference 
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Downloads (6 Weeks): 13,   Downloads (12 Months): 115,   Citation Count: 10
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ABSTRACT

The merging of optimization and simulation technologies has seen a rapid growth in recent years. A Google search on "Simulation Optimization" returns more than six thousand pages where this phrase appears. The content of these pages ranges from articles, conference presentations and books to software, sponsored work and consultancy. This is an area that has sparked as much interest in the academic world as in practical settings. In this paper, we first summarize some of the most relevant approaches that have been developed for the purpose of optimizing simulated systems. We then concentrate on the metaheuristic black-box approach that leads the field of practical applications and provide some relevant details of how this approach has been implemented and used in commercial software. Finally, we present an example of simulation optimization in the context of a simulation model developed to predict performance and measure risk in a real world project selection problem.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Gerencsér, L. 1999. "Optimization Over Discrete Sets Via SPSA," Proceedings of the IEEE Conference on Decision and Control, pp. 1791--1795.
 
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Laguna, M. and R. Martí 2002. "Neural Network Prediction in a System for Optimizing Simulations," IIE Transactions, (34) 3, pp. 273--282.
 
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Martí, R., M. Laguna and V. Campos 2002. "Scatter Search Vs. Genetic Algorithms: An Experimental Evaluation with Permutation Problems", to appear in Adaptive Memory and Evolution: Tabu Search and Scatter Search, Cesar Rego and Bahram Alidaee (eds.), Kluwer Academic Publishers, Boston.
 
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van Beers, W. C. M. and J. P. C. Kleijnen 2003. "Kriging for Interpolation in Random Simulation," Journal of the Operational Research Society, (54) 3, pp. 255--262.

CITED BY  10
 
 
 
 
 
 
 
 
 
 
Collaborative Colleagues:
Jay April: colleagues
Fred Glover: colleagues
James P. Kelly: colleagues
Manuel Laguna: colleagues