Transfer optimization via simultaneous perturbation stochastic approximation

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Transfer optimization via simultaneous perturbation stochastic approximation

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Winter Simulation Conference archive
Proceedings of the 27th conference on Winter simulation table of contents

Arlington, Virginia, United States

Pages: 242 - 249

Year of Publication: 1995

ISBN:0-7803-3018-8

Authors

Stacy D. Hill	The Johns Hopkins University, Applied Physics Laboratory, Laurel, Maryland
Michael C. Fu	College of Business and Management, University of Maryland at College Park, College Park, Maryland

Sponsors

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

Publisher

IEEE Computer Society Washington, DC, USA

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ABSTRACT

We consider the problem of optimizing a transit network with respect to customer service, when simulation of the network is necessary to accurately characterize performance. In particular, we consider the transfer optimization problem, where the goal is to minimize the total expected waiting time of riders by coordinating transfers in the network. We apply the technique of simultaneous perturbation stochastic approximation to optimize system performance. For a simple test case, we provide simulation results and discuss difficulties in applying the technique to this problem, specifically with regard to the smoothness of the objective function.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Bookbinder, J.tt. and D~silets. A., Transfer optimization in a transit network. Transportation Science 26, No. 2, May 1992: 106-118.
	2	Cassandras, C.G. 1993. Discrete Event Systems: Modeling and Performance Analysis. Irwin.
	3	Fu, M.C.. 1994. Optimization via simulation: a review. Annals of Operations Research 53: 199-248.
	4	Michael C. Fu , Yu-Chi Ho, Using perturbation analysis for gradient estimation, averaging and updating in a stochastic approximation algorithm, Proceedings of the 20th conference on Winter simulation, p.509-517, December 12-14, 1988, San Diego, California, United States [doi>10.1145/318123.318244]
	5	Hill, S.D. and M. C. Fu. 1994. Optimizing discrete event systems with the simultaneous perturbation stochastic approximation algorithm, Proceedings of the 33rd IEEE Conference on Decision and Control: 2631-2632.
	6	Stacy D. Hill , Michael C. Fu, Simulation optimization via simultaneous perturbation stochastic approximation, Proceedings of the 26th conference on Winter simulation, p.1461-1464, December 11-14, 1994, Orlando, Florida, United States
	7	Spall, J.C. 1992. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation, IEEE Transactions on Automatic Control 37." 332-341.
	8	Spall, J.C. and J.A. Cristion. 1994. Nonlinear Adaptive Control Using Neural Networks: Estimation with a Smoothed Simultaneous Perturbation Gradient Approximation, Stat~stzca Sinica ~:1-27.