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A review of machine learning in dynamic scheduling of flexible manufacturing systems

  • PAOLO PRIORE (a1), DAVID DE LA FUENTE (a1), ALBERTO GOMEZ (a1) and JAVIER PUENTE (a1)
    • Published online: 01 July 2001
Abstract

A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no single rule exists that is better than the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a review of the main machine learning-based scheduling approaches described in the literature is presented.

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Corresponding author
Address correspondence and reprint requests to: Paolo Priore, ETSII e II, Campus de Viesques, 33203 Gijón, Spain. E-mail: priore@etsiig.uniovi.es
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AI EDAM
  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
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