Abstract
The objective of this study is to develop a neural network based decision support system for selection of appropriate dispatching rules for a real-time manufacturing system, in order to obtain the desired performance measures given by a user, at different scheduling periods. A simulation experiment is integrated with a neural network to obtain the multi-objective scheduler, where simulation is used to provide the training data. The proposed methodology is illustrated on a flexible manufacturing system (FMS) which consists of several number of machines and jobs, loading/unloading stations and automated guided vehicles (AGVs) to transport jobs from one location to another.
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© 2008 Springer-Verlag Berlin Heidelberg
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Akyol, D.E., Araz, O.U. (2008). A Neural Network Based Decision Support System for Real-Time Scheduling of Flexible Manufacturing Systems. In: Kalcsics, J., Nickel, S. (eds) Operations Research Proceedings 2007. Operations Research Proceedings, vol 2007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77903-2_13
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DOI: https://doi.org/10.1007/978-3-540-77903-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77902-5
Online ISBN: 978-3-540-77903-2
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