High order neural networks to control manufacturing systems-a comparison study

Author

Rovithakis, George A. ; Gaganis, Vassilis I. ; Perrakis, Stelios E. ; Christodoulou, Manolis A.

Author_Institution

Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece

Volume

3

fYear

1998

fDate

1998

Firstpage

2736

Abstract

In this paper the neuro adaptive scheduling methodology 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 highly complex nonacyclic FMS, with extremely heterogenous part processing times. The results reveal superiority of our algorithm in terms of backlogging and inventory cost, system stability and work-in-process

Keywords

adaptive systems; computer aided production planning; neural nets; production control; WIP; backlogging cost; heterogenous part processing times; high-order neural networks; highly complex nonacyclic FMS; inventory cost; manufacturing cell; manufacturing system control; neuro adaptive scheduling methodology; system stability; work-in-process; Buffer storage; Control systems; Job shop scheduling; Manufacturing processes; Manufacturing systems; Neural networks; Production; Raw materials; Scheduling algorithm; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1998. Proceedings of the 37th IEEE Conference on

Conference_Location

Tampa, FL

ISSN

0191-2216

Print_ISBN

0-7803-4394-8

Type

conf

DOI

10.1109/CDC.1998.757868

Filename

757868