A neural network approach for the real time control of a FMS

Author

Hao, Gang ; Shang, Jan S. ; Vargas, Luis G.

Author_Institution

Dept. of Appl. Stat. & OR, City Polytech. of Hong Kong, Kowloon, Hong Kong

Volume

3

fYear

1994

fDate

4-7 Jan. 1994

Firstpage

641

Lastpage

648

Abstract

We propose a three phased model of flexible manufacturing system control. The first phase is to identify the feasibility of job moves under a given system status. The simple Sigma-Pi type of neural net model has been adopted in Phase I for feasibility recognition. The second phase applies the Hopfield-Tank model to determine the most appropriate job moves from a feasible job set derived from Phase I. This problem is considered to be very difficult not only because of its NP-complete feature, but also because of the need for quick response under a real-time environment. A Hopfield-Tank network with a linear energy function is proposed for Phase II. Phase III devotes to routing decisions for MHS. A heuristic algorithm based on Kohonen´s self-organizing feature maps is proposed.<>

Keywords

flexible manufacturing systems; genetic algorithms; neural nets; real-time systems; self-organising feature maps; Hopfield-Tank model; NP-complete; Sigma-Pi type; feasibility recognition; flexible manufacturing system control; heuristic algorithm; linear energy function; neural net model; neural network approach; quick response; real time control; routing decisions; self-organizing feature maps; three phased model;

fLanguage

English

Publisher

ieee

Conference_Titel

System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on

Conference_Location

Wailea, HI, USA

Print_ISBN

0-8186-5090-7

Type

conf

DOI

10.1109/HICSS.1994.323317

Filename

323317