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

Volume

9

Issue

2

fYear

2001

fDate

3/1/2001 12:00:00 AM

Firstpage

261

Lastpage

270

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;

fLanguage

English

Journal_Title

Control Systems Technology, IEEE Transactions on

Publisher

ieee

ISSN

1063-6536

Type

jour

DOI

10.1109/87.911378

Filename

911378