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Sample-path optimization of convex stochastic performance functions

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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.

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Authors and Affiliations

  1. Department of Industrial Engineering, University of Wisconsin-Madison, 1513 University Avenue, 53706-1539, Madison, WI, USA

    Erica L. Plambeck, Bor-Ruey Fu, Stephen M. Robinson & Rajan Suri

Authors
  1. Erica L. Plambeck
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  2. Bor-Ruey Fu
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  3. Stephen M. Robinson
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  4. Rajan Suri
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Additional information

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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Plambeck, E.L., Fu, BR., Robinson, S.M. et al. Sample-path optimization of convex stochastic performance functions. Mathematical Programming 75, 137–176 (1996). https://doi.org/10.1007/BF02592150

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  • DOI: https://doi.org/10.1007/BF02592150

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