Article

Practical introduction to simulation optimization

Authors:

Jay April,

Fred Glover,

James P. Kelly,

Manuel LagunaAuthors Info & Claims

WSC '03: Proceedings of the 35th conference on Winter simulation: driving innovation

Pages 71 - 78

Published: 07 December 2003 Publication History

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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

[1]

Andradóttir, S. 1998. "A Review of Simulation Optimization Techniques," in Proceedings of the 1998 Winter Simulation Conference, D. J. Medeiros, E. F. Watson, J. S. Carson and M. S. Manivannan (eds.), pp. 151--158.

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[2]

Fu, M. 2002. "Optimization for Simulation: Theory and Practice," INFORMS Journal on Computing, 14 (3) pp. 192--215.

Digital Library

Google Scholar

[3]

Gerencsér, L. 1999. "Optimization Over Discrete Sets Via SPSA," Proceedings of the IEEE Conference on Decision and Control, pp. 1791--1795.

Google Scholar

[4]

Laguna, M. and R. Martí 2002. "Neural Network Prediction in a System for Optimizing Simulations," IIE Transactions, (34) 3, pp. 273--282.

Crossref

Google Scholar

[5]

Law, A. M. and W. D. Kelton 1991. Simulation Modeling and Analysis, Second Edition, McGraw-Hill, New York.

Digital Library

Google Scholar

[6]

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.

Google Scholar

[7]

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.

Crossref

Google Scholar

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Seiringer WAltendorfer KCastaneda JPanadero JJuan A(2021)Applying simheuristics for safety stock and planned lead time optimization in a rolling horizon MRP system under uncertaintyProceedings of the Winter Simulation Conference10.5555/3522802.3523003(1-12)Online publication date: 13-Dec-2021
https://dl.acm.org/doi/10.5555/3522802.3523003
Barrios BJuan APanadero JAltendorfer KPeirleitner AEstrada-Moreno A(2020)On the use of simheuristics to optimize safety-stock levels in material requirements planning with random demandsProceedings of the Winter Simulation Conference10.5555/3466184.3466356(1539-1550)Online publication date: 14-Dec-2020
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Jerbi AAmmar AKrid MSalah B(2020)Performance optimization of a flexible manufacturing system using simulationSimulation10.1177/003754971881980495:11(1085-1096)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1177/0037549718819804
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Practical introduction to simulation optimization

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WSC '03: Proceedings of the 35th conference on Winter simulation: driving innovation

December 2003

2094 pages

ISBN:0780381327

General Chair:
David Ferrin
Naperville, IL
,
Program Chair:
Douglas J. Morrice
The University of Texas at Austin, Austin, TX

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Winter Simulation Conference

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Published: 07 December 2003

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WSC03: Winter Simulation Conference 2003

December 7 - 10, 2003

Louisiana, New Orleans

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WSC '03 Paper Acceptance Rate 128 of 189 submissions, 68%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Seiringer WAltendorfer KCastaneda JPanadero JJuan A(2021)Applying simheuristics for safety stock and planned lead time optimization in a rolling horizon MRP system under uncertaintyProceedings of the Winter Simulation Conference10.5555/3522802.3523003(1-12)Online publication date: 13-Dec-2021
https://dl.acm.org/doi/10.5555/3522802.3523003
Barrios BJuan APanadero JAltendorfer KPeirleitner AEstrada-Moreno A(2020)On the use of simheuristics to optimize safety-stock levels in material requirements planning with random demandsProceedings of the Winter Simulation Conference10.5555/3466184.3466356(1539-1550)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466356
Jerbi AAmmar AKrid MSalah B(2020)Performance optimization of a flexible manufacturing system using simulationSimulation10.1177/003754971881980495:11(1085-1096)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1177/0037549718819804
Juan AKelton WCurrie CFaulin JJohansson BJain S(2018)Simheuristics applicationsProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320878(3048-3059)Online publication date: 9-Dec-2018
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(2017)A novel Iterative Optimization-based Simulation (IOS) frameworkComputers and Industrial Engineering10.1016/j.cie.2017.06.037111:C(1-17)Online publication date: 1-Sep-2017
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Karlsson INg ASyberfeldt ABandaru S(2015)An interactive decision support system using simulation-based optimization and data miningProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888859(2112-2123)Online publication date: 6-Dec-2015
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Lal TRoh THuschka T(2015)Simulation based optimizationProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888760(1261-1271)Online publication date: 6-Dec-2015
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Gavanelli MNonato MPeano AAlvisi SFranchini M(2015)Scheduling countermeasures to contamination events by genetic algorithmsAI Communications10.5555/2733572.273357928:2(259-282)Online publication date: 1-Apr-2015
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Angelidis EBohn DRose OHill RKuhl M(2013)A simulation tool for complex assembly lines with multi-skilled resourcesProceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World10.5555/2675983.2676303(2577-2586)Online publication date: 8-Dec-2013
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Introduction to simulation

Simulation optimization: a survey of simulation optimization techniques and procedures

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