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Use of a classifier in a knowledge‐based simulation optimization system

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  • Ingrid W. M. Crouch
  • Allen G. Greenwood
  • Loren Paul Rees

Abstract

This article defines and develops a simulation optimization system based upon response surface classification and the integration of multiple search strategies. Response surfaces are classified according to characteristics that indicate which search technique will be most successful. Typical surface characteristics include statistical measures and topological features, while search techniques encompass response surface methodology, simulated annealing, random search, etc. The classify‐then‐search process flow and a knowledge‐based architecture are developed and then demonstrated with a detailed computer example. The system is useful not only as an approach to optimizing simulations, but also as a means for integrating search techniques and thereby providing the user with the most promising path toward an optimal solution. © 1995 John Wiley & Sons, Inc.

Suggested Citation

  • Ingrid W. M. Crouch & Allen G. Greenwood & Loren Paul Rees, 1995. "Use of a classifier in a knowledge‐based simulation optimization system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 42(8), pages 1203-1232, December.
    • Handle: RePEc:wly:navres:v:42:y:1995:i:8:p:1203-1232
    DOI: 10.1002/1520-6750(199512)42:83.0.CO;2-8
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    References listed on IDEAS

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    1. Dennis E. Smith, 1973. "An Empirical Investigation of Optimum-Seeking in the Computer Simulation Situation," Operations Research, INFORMS, vol. 21(2), pages 475-497, April.
    2. M. Hossein Safizadeh, 1990. "Optimization in simulation: Current issues and the future outlook," Naval Research Logistics (NRL), John Wiley & Sons, vol. 37(6), pages 807-825, December.
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