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A comparison of perturbation analysis techniques

Published: 08 November 1996 Publication History

Abstract

Perturbation analysis (PA) is a technique for estimating gradients of performance measures, particularly applicable to the simulation of discrete-event systems. Over the past two decades, various "versions" have been developed. In this paper, we compare and contrast some of these perturbation analysis techniques by applying them to a simple example. This example also serves to highlight the issue of process representation that can play a very crucial role in the application of perturbation analysis.

References

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Br~maud, P. and F.J. V~zquez-Abad. 1992. On the pathwise computation of derivatives with respect to the rate of a point process: the phantom RPA method, Queueing Systems: Theory and Applications 10: 249-270.
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Dai, L. and Y.C. Ho. 1995. Structural infinitesimal perturbation analysis for derivative estimation in discrete event dynamic systems, IEEE Transactions on Automatic Control 40:1154-1166.
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Fu, M.C. 1994. Optimization via simulation: a review. Annals of Operations Research 53: 199-248.
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Fu, M.C. and J. Q. Hu. 1992. Extensions and generalizations of smoothed perturbation analysis in a generalized semi-Markov process framework, IEEE Transactions on Automatic Control 37: 1483-1500.
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Fu, M.C. and J. Q. Hu. 1996. Conditional Monte Carlo: Gradient Estimation and Optimization Applications, Kluwer Academic Publishers, forthcoming.
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Gaivoronski, A., L.Y. Shi, and R.S. Sreenivas. 1992. Augmented infinitesimal perturbation analysis: an alternate explanation, Discrete Event Dynamic Systems: Theory and Applications, 2: 121-138.
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Glasserman, P. 1991. Gradient Estimation Via Perturbation Analysis, Kluwer Academic.
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Gong, W.B. and Y.C. Ho. 1987. Smoothed perturbation analysis of discrete-event dynamic systems, IEEE Transactions on Automatic Control 32: 858- 867.
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Cited By

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  • (2006)Gradient-based simulation optimizationProceedings of the 38th conference on Winter simulation10.5555/1218112.1218146(159-167)Online publication date: 3-Dec-2006
  • (2006)Gradient-Based Simulation OptimizationProceedings of the 2006 Winter Simulation Conference10.1109/WSC.2006.323048(159-167)Online publication date: Dec-2006

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cover image ACM Conferences
WSC '96: Proceedings of the 28th conference on Winter simulation
November 1996
1527 pages
ISBN:0780333837

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IEEE Computer Society

United States

Publication History

Published: 08 November 1996

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WSC90
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  • IIE
  • INFORMS/CS
  • IEEE-SMCS
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  • ACM
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  • SIGSIM
  • IEEE-CS
  • NIST
WSC90: 1990 Winter Simulation Conference
December 8 - 11, 1996
California, Coronado, USA

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WSC '96 Paper Acceptance Rate 128 of 187 submissions, 68%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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View all
  • (2006)Gradient-based simulation optimizationProceedings of the 38th conference on Winter simulation10.5555/1218112.1218146(159-167)Online publication date: 3-Dec-2006
  • (2006)Gradient-Based Simulation OptimizationProceedings of the 2006 Winter Simulation Conference10.1109/WSC.2006.323048(159-167)Online publication date: Dec-2006

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