Article

A testbed of simulation-optimization problems

Authors:

Raghu Pasupathy,

Shane G. HendersonAuthors Info & Claims

WSC '06: Proceedings of the 38th conference on Winter simulation

Pages 255 - 263

Published: 03 December 2006 Publication History

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Abstract

We propose a testbed of simulation-optimization problems. The purpose of the testbed is to encourage development and constructive comparison of simulation-optimization techniques and algorithms. We are particularly interested in increasing attention to the finite-time performance of algorithms, rather than the asymptotic results that one often finds in the literature.

References

[1]

Barton, R. R., and M. Meckesheimer. 2006. Metamodelbased simulation optimization. In Handbook of Simulation, ed. S. G. Henderson and B. L. Nelson, 535--574. Elsevier.

Google Scholar

[2]

Crowder, H. P., R. S. Dembo, and J. M. Mulvey. 1979. On reporting computational experiments with mathematical software. ACM Transactions on Mathematical Software 5:193--203.

Digital Library

Google Scholar

[3]

Dolan, E. D., and J. J. Moré. 2002. Benchmarking optimization software with performance profiles. Mathematical Programming, Series A 91:201--213.

Crossref

Google Scholar

[4]

Fu, M. C. 2002. Optimization for simulation: theory vs. practice. INFORMS Journal on Computing 14:192--215.

Digital Library

Google Scholar

[5]

Fu, M. C., S. Andradóttir, J. S. Carson, F. G. J. P. Kelly, and S. M. Robinson. 2000. Integrating optimization and simulation: research and practice. In Proceedings of the 2000 Winter Simulation Conference, ed. J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, 610--616.

Digital Library

Google Scholar

[6]

Glynn, P. W. 2002. Additional perspectives on simulation for optimization. INFORMS Journal on Computing 14:220--222.

Digital Library

Google Scholar

[7]

Ho, Y. C., R. Sreenivas, and P. Vakili. 1992. Ordinal optimization of discrete event dynamic systems. Journal of Discrete-Event Dynamic Systems 2 (2): 61--88.

Crossref

Google Scholar

[8]

Jackson, R. H. F., P. T. Boggs, S. G. Nash, and S. Powell. 1991. Guidelines for reporting results of computational experiments. Report of the ad hoc committee. Mathematical Programming 49 (3): 413--425.

Digital Library

Google Scholar

[9]

Lee, S. H. 1998. Monte Carlo computation of conditional expectation quantiles. Ph.D. thesis, Stanford University, Stanford, CA.

Digital Library

Google Scholar

[10]

Mak, W.-K., D. P. Morton, and R. K. Wood. 1999. Monte Carlo bounding techniques for determining solution quality in stochastic programs. Operations Research Letters 24:47--56.

Digital Library

Google Scholar

[11]

Montaz Ali, M., C. Khompatraporn, and Z. B. Zabinsky. 2005. A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. Journal of Global Optimization 31:635--672.

Digital Library

Google Scholar

[12]

Mulvey, J. M. (Ed.) 1982. Evaluating mathematical programming techniques, Volume 199 of Lecture Notes in Economics and Mathematical Systems. New York: Springer-Verlag.

Google Scholar

Cited By

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Hunter SOndes B(2023)Properties of Several Performance Indicators for Global Multi-Objective Simulation OptimizationProceedings of the Winter Simulation Conference10.5555/3643142.3643441(3577-3588)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643441
Hunter SPasupathy R(2022)Central Limit Theorems for Constructing Confidence Regions in Strictly Convex Multi-Objective Simulation OptimizationProceedings of the Winter Simulation Conference10.5555/3586210.3586464(3015-3026)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586464
Henderson S(2021)Reflections on simulation optimizationProceedings of the Winter Simulation Conference10.5555/3522802.3522803(1-15)Online publication date: 13-Dec-2021
https://dl.acm.org/doi/10.5555/3522802.3522803
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A testbed of simulation-optimization problems
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Information & Contributors

Information

Published In

WSC '06: Proceedings of the 38th conference on Winter simulation

December 2006

2429 pages

ISBN:1424405017

Editors:
L. Felipe Perrone
Bucknell University
,
Barry G. Lawson
University of Richmond
,
Jason Liu
Colorado School of Mines
,
Frederick P. Wieland
Sensis Corporation
,
General Chair:
David Nicol
University of Illinois, Urbana-Champaign
,
Program Chair:
Richard Fujimoto
College of Computing, Georgia Tech

Publisher

Winter Simulation Conference

Publication History

Published: 03 December 2006

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Conference

WSC06

Sponsor:

IIE
ASA
IEICE ESS
IEEE-CS\DATC
SIGSIM
NIST
(SCS)
INFORMS-CS

WSC06: Winter Simulation Conference 2006

December 3 - 6, 2006

California, Monterey

Acceptance Rates

WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;

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

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Cited By

View all

Hunter SOndes B(2023)Properties of Several Performance Indicators for Global Multi-Objective Simulation OptimizationProceedings of the Winter Simulation Conference10.5555/3643142.3643441(3577-3588)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643441
Hunter SPasupathy R(2022)Central Limit Theorems for Constructing Confidence Regions in Strictly Convex Multi-Objective Simulation OptimizationProceedings of the Winter Simulation Conference10.5555/3586210.3586464(3015-3026)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586464
Henderson S(2021)Reflections on simulation optimizationProceedings of the Winter Simulation Conference10.5555/3522802.3522803(1-15)Online publication date: 13-Dec-2021
https://dl.acm.org/doi/10.5555/3522802.3522803
Applegate EHunter SSon YHaas P(2019)R-MGSPLINEProceedings of the Winter Simulation Conference10.5555/3400397.3400682(3516-3527)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3400397.3400682
Eckman DHenderson SPasupathy RSon YHaas P(2019)Redesigning a testbed of simulation-optimization problems and solvers for experimental comparisonsProceedings of the Winter Simulation Conference10.5555/3400397.3400677(3457-3467)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3400397.3400677
Hunter SApplegate EArora VChong BCooper KRincón-Guevara OVivas-Valencia C(2019)An Introduction to Multiobjective Simulation OptimizationACM Transactions on Modeling and Computer Simulation10.1145/329987229:1(1-36)Online publication date: 24-Jan-2019
https://dl.acm.org/doi/10.1145/3299872
Cooper KHunter SNagaraj K(2017)An epsilon-constraint method for integer-ordered bi-objective simulation optimizationProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242375(1-12)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242375
Dong NEckman DZhao XHenderson SPoloczek M(2017)Empirically comparing the finite-time performance of simulation-optimization algorithmsProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242366(1-12)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242366
Fu MHenderson S(2017)History of seeking better solutions, aka simulation optimizationProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242192(1-27)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242192
Shashaani SHunter SPasupathy RHuschka TChick S(2016)ASTRO-DFProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042176(554-565)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042176
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