Title :
Application of a neural-network scheduler on a real manufacturing system
Author :
Rovithakis, George A. ; Perrakis, S.E. ; Christodoulou, Manolis A.
Author_Institution :
Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece
fDate :
3/1/2001 12:00:00 AM
Abstract :
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
Keywords :
computer aided production planning; manufacturing data processing; neural nets; production control; real-time systems; adaptive machine scheduling; backlogging; inventory cost; job shop; manufacturing system; neural-network; real time systems; system stability; work in process; Artificial intelligence; Computer industry; Control systems; Control theory; Costs; Dynamic scheduling; Job shop scheduling; Manufacturing processes; Manufacturing systems; Production systems;
Journal_Title :
Control Systems Technology, IEEE Transactions on
