An artificial neural network optimized by a genetic algorithm for real-time flow-shop scheduling

Author

Abe, Masahiko ; Matsumoto, Hideyuki ; Kuroda, Chiaki

Author_Institution

Graduate Sch. of Sci. & Technol., Tokyo Inst. of Technol., Japan

Volume

1

fYear

2000

fDate

2000

Firstpage

329

Abstract

A job-shop scheduling method using a three-layered neural network optimized by a genetic algorithm, which is called a GANN (genetic algorithm neural net) scheduling method, is a flexible and practical quasi-optimal scheduling method. However, further improvements of the present GANN scheduling system are required for rapid flow-shop rescheduling in chemical processes for multi-purpose production. In this study, we investigated the effect of improvements to the GANN scheduling system on the efficiency of rescheduling when new jobs were appended in a chemical process with some buffer tanks. The results showed that the former GANN scheduling method could be developed into a practical real-time scheduling system for process problems

Keywords

chemical engineering computing; genetic algorithms; neural nets; production control; production engineering computing; real-time systems; scheduling; GANN scheduling system; artificial neural network optimization; buffer tanks; chemical processes; flow-shop rescheduling efficiency; genetic algorithm; job-shop scheduling method; multi-purpose production; quasi-optimal scheduling method; real-time flow-shop scheduling; Artificial neural networks; Biological cells; Chemical engineering; Chemical processes; Chemical technology; Costs; Genetic algorithms; Optimization methods; Optimized production technology; Real time systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on

Conference_Location

Brighton

Print_ISBN

0-7803-6400-7

Type

conf

DOI

10.1109/KES.2000.885823

Filename

885823