Abstract
This paper describes experiments performed to demonstrate the feasibility of applying human learning and machine induction to reactive scheduling of an unbalanced telephone production line in a simulated environment. The simulation model is used to accelerate human training, to log the scheduling decisions of a so trained human expert, and to form the basis for the generation of machine learned decision rules which replicate the human’s ability to optimise the performance of the plant.
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© 1995 IFIP International Federation for Information Processing
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Kerr, R.M., Kibira, D. (1995). Learning to schedule and unbalance production using simulation and rule induction. 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_9
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DOI: https://doi.org/10.1007/978-0-387-34928-2_9
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-5041-2889-6
Online ISBN: 978-0-387-34928-2
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