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A gradient approach for smartly allocating computing budget for discrete event simulation

Authors:

Chun-Hung Chen,

Hsiao-Chang Chen, and

Liyi DaiAuthors Info & Claims

WSC '96: Proceedings of the 28th conference on Winter simulation

November 1996

Pages 398 - 405

https://doi.org/10.1145/256562.256696

Published: 08 November 1996 Publication History

Abstract

Simulation plays a vital role in analyzing many discrete event systems. Usually, using simulation to solve such problems can be both expensive and time consuming. We present an effective approach to smartly allocate computing budget for discrete-event simulation. This approach can smartly determine the best simulation lengths for all simulation experiments and significantly reduce the total computation cost for obtaining the same confidence level. Numerical testing shows that our approach can obtain the same simulation quality with one-tenth the simulation effort.

References

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Casella, G., and R. L. Berger. 1990. Statistical Inference, Wadsworth.

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Chen, C. H. 1995. "An Effective Approach to Smartly Allocate Computing Budget for Discrete Event Simulation," Proceedings of the 34th 1EEE Conference on Decision and Control, 2598-2605.

Digital Library

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Chen, C. H. 1996. "A Lower Bound for the Correct Subset-Selection Probability and Its Application to Discrete Event System Simulations," To appear on IEEE Transactions on Automatic Control.

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Chen, C. H., and Y. C. Ho. 1995. "An Approximation Approach of the Standard Clock Method for General Discrete Event Simulation," IEEE Transactions on Control Systems Technology, Vol. 3, #3, 309-317.

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Goldsman, G., B. L. Nelson, and B. Schmeiser. 1991. "Methods for Selecting the Best System," Proceedings of the 1991 Winter Simulation Conference, 177-186.

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Cited By

Powell W(2022)Derivative‐Free Stochastic SearchReinforcement Learning and Stochastic Optimization10.1002/9781119815068.ch7(317-427)Online publication date: 8-Apr-2022
https://doi.org/10.1002/9781119815068.ch7
Powell W(2022)Canonical Problems and ApplicationsReinforcement Learning and Stochastic Optimization10.1002/9781119815068.ch2(39-100)Online publication date: 8-Apr-2022
https://doi.org/10.1002/9781119815068.ch2
Wang YTang WYao YZhu F(2019)DA-OCBA: Distributed Asynchronous Optimal Computing Budget Allocation Algorithm of Simulation Optimization Using Cloud ComputingSymmetry10.3390/sym1110129711:10(1297)Online publication date: 15-Oct-2019
https://doi.org/10.3390/sym11101297
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Published In

cover image ACM Conferences

WSC '96: Proceedings of the 28th conference on Winter simulation

November 1996

1527 pages

ISBN:0780333837

Editors:
John M. Charnes
Univ. of Kansas, Lawrence
,
Douglas J. Morrice
Univ. of Texas, Austin
,
Daniel T. Brunner
Systemflow Simulations, Inc., Indianapolis, IN
,
James J. Swain
Univ. of Alabama, Huntsville, Indianapolis, IN

Sponsors

IIE: Institute of Industrial Engineers
INFORMS/CS: Computer Science TC
IEEE-SMCS: Systems, Man & Cybernetics Society
ASA: American Statistical Association
ACM: Association for Computing Machinery
SCS: Society for Computer Simulation
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IEEE-CS: Computer Society
NIST: National Institute of Standards & Technology

Publisher

IEEE Computer Society

United States

Publication History

Published: 08 November 1996

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WSC90

Sponsor:

IIE
INFORMS/CS
IEEE-SMCS
ASA
ACM
SCS
SIGSIM
IEEE-CS
NIST

WSC90: 1990 Winter Simulation Conference

December 8 - 11, 1996

California, Coronado, USA

Acceptance Rates

WSC '96 Paper Acceptance Rate 128 of 187 submissions, 68%;

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

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Citations

Cited By

Powell W(2022)Derivative‐Free Stochastic SearchReinforcement Learning and Stochastic Optimization10.1002/9781119815068.ch7(317-427)Online publication date: 8-Apr-2022
https://doi.org/10.1002/9781119815068.ch7
Powell W(2022)Canonical Problems and ApplicationsReinforcement Learning and Stochastic Optimization10.1002/9781119815068.ch2(39-100)Online publication date: 8-Apr-2022
https://doi.org/10.1002/9781119815068.ch2
Wang YTang WYao YZhu F(2019)DA-OCBA: Distributed Asynchronous Optimal Computing Budget Allocation Algorithm of Simulation Optimization Using Cloud ComputingSymmetry10.3390/sym1110129711:10(1297)Online publication date: 15-Oct-2019
https://doi.org/10.3390/sym11101297
Görder BKolonko M(2019)Ranking and SelectionACM Transactions on Modeling and Computer Simulation10.1145/324104229:1(1-24)Online publication date: 24-Jan-2019
https://dl.acm.org/doi/10.1145/3241042
Shin DBroadie MZeevi A(2018)Tractable Sampling Strategies for Ordinal OptimizationOperations Research10.1287/opre.2018.175366:6(1693-1712)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1287/opre.2018.1753
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
Zhang XDing LHuschka TChick S(2016)Sequential sampling for Bayesian robust ranking and selectionProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042200(758-769)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042200
Chen SReyes KGupta MMcAlpine MPowell W(2015)Optimal Learning in Experimental Design Using the Knowledge Gradient Policy with Application to Characterizing Nanoemulsion StabilitySIAM/ASA Journal on Uncertainty Quantification10.1137/1409711293:1(320-345)Online publication date: Jan-2015
https://doi.org/10.1137/140971129
Bastani MThanos ACelik NChen CBuckley SMiller J(2014)Efficient design selection in microgrid simulationsProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2694199(2762-2773)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2694199
Melouk SFontem BWaymire EHall S(2014)Stochastic resource allocation using a predictor-based heuristic for optimization via simulationComputers and Operations Research10.1016/j.cor.2013.12.01046(38-48)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1016/j.cor.2013.12.010
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