Abstract
In this paper we report on the development and use of a tool for the acquisition of heuristics used by operators of a glass coater in reactive scheduling. These results complement previous studies of neural network process models in scheduling production for the glass coating operation (AIRCO) at Pilkington Australia. Integration of our knowledge rich control strategies in the construction of an Expert Adviser for scheduling is now in progress.
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B.J. Garner and G. J. Ridley and P. J. Lower, Data Engineering for Neural Net Analysis of Glass Furnace Characteristics. Proceedings of the ANNES Conference, New Zealand, (1993).
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© 1995 IFIP International Federation for Information Processing
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Garner, B.J., Ridley, G.J. (1995). Knowledge acquisition for reactive scheduling. In: Kerr, R., Szelke, E. (eds) Artificial Intelligence in Reactive Scheduling. IFIP Advances in Information and Communication Technology. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-34928-2_12
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DOI: https://doi.org/10.1007/978-0-387-34928-2_12
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-5041-2889-6
Online ISBN: 978-0-387-34928-2
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