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Application of a neural-network scheduler on a real manufacturing system

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3 Author(s)
Rovithakis, G.A. ; Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece ; Perrakis, S.E. ; Christodoulou, M.A.

In this paper a neural adaptive scheduling methodology approached machine scheduling as a control regulation problem is evaluated by comparing its performance with conventional schedulers, through simulation studies. The case study chosen constitutes an existing manufacturing cell which can be viewed as a deterministic job shop with extremely heterogenous part processing times. The results facilitate a thorough assessment of our algorithm in terms of the backlogging and inventory cost, system stability, and work in process

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Control Systems Technology, IEEE Transactions on  (Volume:9 ,  Issue: 2 )