Article

Free access

Iterative ranking-and-selection for large-scale optimization

Author:

Sigurdur ÓlafssonAuthors Info & Claims

WSC '99: Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1

Pages 479 - 485

https://doi.org/10.1145/324138.324302

Published: 01 December 1999 Publication History

PDF eReader

References

[1]

Andrad~ ottir, S. 1998. "A Review of Simulation Optimiza-tion Techniques," in Proceedings of the 1998 Winter Simulation Conference, 151-158.

Digital Library

Google Scholar

[2]

Chen, C.-H., E. Y~ ucesan, Y. Yuan, H.-C. Chen, and L. Dai. 1998. "Computing Budget Allocation for Simulation Experiments with Different System Structures," in Pro-ceedings of the 1998 Winter Simulation Conference, 735-741.

Digital Library

Google Scholar

[3]

Chick, S.E. 1997. "Selecting the Best System: A Decision Theoretic Approach," in Proceedings of the 1997 Winter Simulation Conference, 326-333.

Digital Library

Google Scholar

[4]

Cox, D.R. and H.D. Miller, 1965. The Theory of Stochastic Processes, Chapman & Hall, London.

Google Scholar

[5]

Fu, M.C. 1994. "Stochastic Optimization using Simulation: A review," Annals of Operations Research, 53, 199-248.

Crossref

Google Scholar

[6]

Goldsman, D. and B.L. Nelson. 1998. "Statistical Screen-ing, Selection, and Multiple Comparison Procedures in Computer Simulation," in Proceedings of the 1998 Winter Simulation Conference, 159-166.

Digital Library

Google Scholar

[7]

Jacobson, S.H. and L.W. Schruben. 1989. "A Review of Techniques for Simulation Optimization," Operations Research Letters, 8, 1-9.

Digital Library

Google Scholar

[8]

Matejcik, F.J. and B.L. Nelson. 1995. "Two-Stage Multiple Comparisons with the Best for Computer Simulation," Operations Research, 43, 633-640.

Digital Library

Google Scholar

[9]

~ Olafsson, S. and L. Shi. 1998. "A Method for Scheduling in Parallel Manufacturing Systems with Flexible Re-sources," IIE Transactions on Scheduling and Logistics, accepted for publication.

Google Scholar

[10]

Rinott, Y. 1978. "On Two-Stage Selection Procedures and Related Probability-in-Equalities," Communications in Statistics, A7, 799-811.

Crossref

Google Scholar

[11]

Shi, L. and S. ~ Olafsson. 1997. "An Integrated Framework for Deterministic and Stochastic Optimization," in Pro-ceedings of the 1997 Winter Simulation Conference, 358-365.

Digital Library

Google Scholar

[12]

Shi, L. and S. ~ Olafsson. 1998a. "Nested Partitions Method for Global Optimization," Operations Research, accepted for publication.

Digital Library

Google Scholar

[13]

Shi, L. and S. ~ Olafsson. 1998b. "Nested Partitions Method for Stochastic Optimization," Working Paper Series, 98-116, Department of Industrial and Manufacturing Systems Engineering, Iowa State University.

Google Scholar

Cited By

View all

Eckman DHenderson S(2018)Reusing Search Data in Ranking and SelectionACM Transactions on Modeling and Computer Simulation10.1145/317050328:3(1-15)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3170503
Sriver TChrissis JAbramson M(2009)Pattern search ranking and selection algorithms for mixed variable simulation-based optimizationEuropean Journal of Operational Research10.1016/j.ejor.2008.10.020198:3(878-890)Online publication date: Nov-2009
https://doi.org/10.1016/j.ejor.2008.10.020
Alrefaei MDiabat A(2009)A simulated annealing technique for multi-objective simulation optimizationApplied Mathematics and Computation10.1016/j.amc.2009.09.051215:8(3029-3035)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.1016/j.amc.2009.09.051
Show More Cited By

Index Terms

Iterative ranking-and-selection for large-scale optimization

Recommendations

Primal-Dual Active-Set Methods for Large-Scale Optimization

In this paper, we introduce two primal-dual active-set methods for solving large-scale constrained optimization problems. The first method minimizes a sequence of primal-dual augmented Lagrangian functions subject to bounds on the primal variables and ...
Optimal subgradient algorithms for large-scale convex optimization in simple domains

This paper describes two optimal subgradient algorithms for solving structured large-scale convex constrained optimization. More specifically, the first algorithm is optimal for smooth problems with Lipschitz continuous gradients and for Lipschitz ...
Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization

In this paper we analyze several new methods for solving nonconvex optimization problems with the objective function consisting of a sum of two terms: one is nonconvex and smooth, and another is convex but simple and its structure is known. Further, we ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

WSC '99: Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1

December 1999

925 pages

ISBN:0780357809

DOI:10.1145/324138

Editors:
Phillip A. Farrington
Univ. of Alabama in Huntsville, Huntsville
,
Harriet Black Nembhard
Univ. of Wisconsin—Madison, Madison
,
David T. Sturrock
Systems Modeling, Sewickley, PA
,
Gerald W. Evans
Univ. of Louisville, Louisville, KY

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 1999

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

WSC99

Sponsor:

WSC99: Winter Simulation Conference 1999

December 5 - 8, 1999

Arizona, Phoenix, USA

Acceptance Rates

WSC '99 Paper Acceptance Rate 139 of 206 submissions, 67%;

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

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
216
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)3

Reflects downloads up to 21 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Eckman DHenderson S(2018)Reusing Search Data in Ranking and SelectionACM Transactions on Modeling and Computer Simulation10.1145/317050328:3(1-15)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3170503
Sriver TChrissis JAbramson M(2009)Pattern search ranking and selection algorithms for mixed variable simulation-based optimizationEuropean Journal of Operational Research10.1016/j.ejor.2008.10.020198:3(878-890)Online publication date: Nov-2009
https://doi.org/10.1016/j.ejor.2008.10.020
Alrefaei MDiabat A(2009)A simulated annealing technique for multi-objective simulation optimizationApplied Mathematics and Computation10.1016/j.amc.2009.09.051215:8(3029-3035)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.1016/j.amc.2009.09.051
Hay LPeng Csuyan Tjuxin L(2009)Application of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimization ProblemsMulti-Objective Memetic Algorithms10.1007/978-3-540-88051-6_5(91-110)Online publication date: 2009
https://doi.org/10.1007/978-3-540-88051-6_5
Lee LChew ETeng SNicol DFujimoto R(2006)Integration of statistical selection with search mechanism for solving multi-objective simulation-optimization problemsProceedings of the 38th conference on Winter simulation10.5555/1218112.1218170(294-303)Online publication date: 3-Dec-2006
https://dl.acm.org/doi/10.5555/1218112.1218170
Ólafsson SYang J(2005)Intelligent Partitioning for Feature SelectionINFORMS Journal on Computing10.1287/ijoc.1040.010417:3(339-355)Online publication date: 1-Jul-2005
https://dl.acm.org/doi/10.1287/ijoc.1040.0104
Kim JÓlafsson SSnowdon JCharnes J(2002)Optimization via simulationProceedings of the 34th conference on Winter simulation: exploring new frontiers10.5555/1030453.1030495(279-284)Online publication date: 8-Dec-2002
https://dl.acm.org/doi/10.5555/1030453.1030495
Ólafsson SKim JSnowdon JCharnes J(2002)Simulation optimizationProceedings of the 34th conference on Winter simulation: exploring new frontiers10.5555/1030453.1030467(79-84)Online publication date: 8-Dec-2002
https://dl.acm.org/doi/10.5555/1030453.1030467
Ólafsson SKim JRohrer MMedeiros DGrabau M(2001)Simulation optimizationProceedings of the 33nd conference on Winter simulation10.5555/564124.564166(300-306)Online publication date: 9-Dec-2001
https://dl.acm.org/doi/10.5555/564124.564166
Ólafsson SGopinath NFishwick PKang KJoines JBarton R(2000)Optimal selection probability in the two-stage nested partitions method for simulation-based optimizationProceedings of the 32nd conference on Winter simulation10.5555/510378.510483(736-742)Online publication date: 10-Dec-2000
https://dl.acm.org/doi/10.5555/510378.510483
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Primal-Dual Active-Set Methods for Large-Scale Optimization

Optimal subgradient algorithms for large-scale convex optimization in simple domains

Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization