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Gradient-Based Optimization Techniques for Discrete Event Systems Simulation

  1. Hossein Arsham

Published Online: 15 SEP 2008

DOI: 10.1002/9780470050118.ecse371

Wiley Encyclopedia of Computer Science and Engineering

Wiley Encyclopedia of Computer Science and Engineering

How to Cite

Arsham, H. 2008. Gradient-Based Optimization Techniques for Discrete Event Systems Simulation. Wiley Encyclopedia of Computer Science and Engineering. 1–17.

Author Information

  1. University of Baltimore, Baltimore, Maryland

Publication History

  1. Published Online: 15 SEP 2008

Abstract

This article considers the design, analysis, and operation of discrete event systems (DES) with performance J(θ) depending on the value of certain continuous decision parameters θεΘ. This sensitivity information is useful for assessing the relative importance of each parameter θ, studying the local functional behavior, and constructing of J(θ) over Θ. In addition to this valuable descriptive information, the sensitivity information is of prime importance in optimization. Techniques for obtaining sensitivity information and optimizing DES via simulation are presented. The article assumes an existing validated and verified simulation application in which the presented algorithms can be incorporated. Postsolution analysis tools, such as stability and “what-if” analyses, are provided. The goal is to provide a survey of the relevant literature and set forth widely used techniques in a synthesized and unified manner. All algorithms are presented in English-like format, and therefore the algorithms can be implemented using a variety of operating systems and machines, which provides unlimited portability.

Keywords:

  • discrete-event systems;
  • gradient estimation;
  • frequency domain;
  • perturbation analysis;
  • likelihood ratio;
  • score function;
  • single-run optimization;
  • post-optimality analysis;
  • gradient and response surface techniques;
  • what-if analysis;
  • system analysis;
  • design and control;
  • sensitivity analysis;
  • goal-seeking problem;
  • inverse problem