The implementation of neural networks for the optimization of the production scheduling

Author

Witkowski, Tadeusz ; Antczak, Pawel ; Strojny, Grzegorz

Author_Institution

Fac. of Production Eng., Warsaw Univ. of Technol., Poland

Volume

3

fYear

2004

fDate

25-29 July 2004

Firstpage

2233

Abstract

The work presents the application of a constraint satisfaction adaptive neural network to job-shop the scheduling problem. The main idea of the CSANN method is described. In particular, the capacity of the net for adaptation to constraints of a specific problem is presented. A computer experiment is conducted to find the Johnson criterion (the minimal total time of the performance of all operations). The criterion is mainly found as a function of the number of iterations of the computing process. Achieved results are compared with the genetic algorithm AGHAR worked out for the solving of such problems.

Keywords

genetic algorithms; iterative methods; job shop scheduling; neural nets; Johnson criterion; constraint satisfaction adaptive neural network; genetic algorithm; iteration methods; job shop scheduling; optimization; production scheduling; Adaptive systems; Artificial neural networks; Electronic mail; Job production systems; Job shop scheduling; Neural networks; Neurons; Optimal scheduling; Optimized production technology; Paper technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1380968

Filename

1380968