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A Simulation and Learning Technique for Generating Knowledge about Manufacturing Systems Behavior

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Artificial Intelligence in Operational Research

Abstract

One of the most important difficulties when developing knowledge based systems in manufacturing scheduling or control, is finding the required knowledge. We address here the problem of acquiring knowledge about the behavior of manufacturing systems. Learning algorithms are proposed to generate, from simulation experiments, a set of production rules. This set may be considered as a simulation meta-model, and may be used either directly by the shop manager, or inserted into a knowledge base. This approach is illustrated by the use of the learning program GENREG. It generates rules related to the behavior of a simplified flow shop when different dispatching rules are applied.

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Author information

Authors and Affiliations

  1. Laboratoire d’Informatique et de Modélisation des Systèmes de Production, Université Claude Bernard Lyon I Villeurbanne, France

    Henri Pierreval

  2. INRIA: Domaine de Voluceau Rocquencourt Le Chesnay, France

    Henri Ralambondrainy

Authors
  1. Henri Pierreval
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  2. Henri Ralambondrainy
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Editor information

Editors and Affiliations

  1. Athens University of Economics and Business, Greece

    Georgios I. Doukidis

  2. London School of Economics and Political Science, UK

    Ray J. Paul

Copyright information

© 1992 Operational Research Society Ltd

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Cite this chapter

Pierreval, H., Ralambondrainy, H. (1992). A Simulation and Learning Technique for Generating Knowledge about Manufacturing Systems Behavior. In: Doukidis, G.I., Paul, R.J. (eds) Artificial Intelligence in Operational Research. Palgrave, London. https://doi.org/10.1007/978-1-349-12362-9_23

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  • DOI: https://doi.org/10.1007/978-1-349-12362-9_23

  • Publisher Name: Palgrave, London

  • Print ISBN: 978-1-349-12364-3

  • Online ISBN: 978-1-349-12362-9

  • eBook Packages: EngineeringEngineering (R0)

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