Sample-path optimization of convex stochastic performance functions |
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Authors: | Erica L Plambeck Bor-Ruey Fu Stephen M Robinson Rajan Suri |
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Institution: | (1) Department of Industrial Engineering, University of Wisconsin-Madison, 1513 University Avenue, 53706-1539 Madison, WI, USA |
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Abstract: | In this paper we propose a method for optimizing convex performance functions in stochastic systems. These functions can include
expected performance in static systems and steady-state performance in discrete-event dynamic systems; they may be nonsmooth.
The method is closely related to retrospective simulation optimization; it appears to overcome some limitations of stochastic
approximation, which is often applied to such problems. We explain the method and give computational results for two classes
of problems: tandem production lines with up to 50 machines, and stochastic PERT (Program Evaluation and Review Technique)
problems with up to 70 nodes and 110 arcs.
Sponsored by the National Science Foundation under grant number CCR-9109345, by the Air Force Systems Command, USAF, under
grant numbers F49620-93-1-0068 and F49620-95-1-0222, by the U.S. Army Research Office under grant number DAAL03-92-G-0408,
and by the U.S. Army Space and Strategic Defense Command under contract number DASG60-91-C-0144. The U.S. Government has certain
rights in this material, and is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding
any copyright notation thereon.
Sponsored by a Wisconsin/Hilldale Research Award, by the U.S. Army Space and Strategic Defense Command under contract number
DASG60-91-C-0144, and the Air Force Systems Command, USAF, under grant number F49620-93-1-0068.
Sponsored by the National Science Foundation under grant number DDM-9201813. |
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Keywords: | Stochastic optimization Steady-state performance Expected performance Discrete event systems Nonsmooth optimization Stochastic convexity Sample-path optimization |
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