Genetic algorithm and adaptive neural network hybrid method for job-shop scheduling problems

Date

1998-07

Advisors

Journal Title

Journal ISSN

ISSN

1001-0920

DOI

Volume Title

Publisher

Northeastern University Press, China

Type

Article

Peer reviewed

Yes

Abstract

This paper proposes a hybrid method of genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) for solving job-shop scheduling problems. In the hybrid method GA is used to iterate for searching optimal solutions, CSANN is used to solve feasible solutions during the iteration of GA. Computer simulations have shown the good performance of the proposed hybrid method for job-shop scheduling problems.

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

Job-shop scheduling, genetic algorithm, constraint satisfaction, adaptive neural network

Citation

Yang, S. and Wang, D. (1998) Genetic algorithm and adaptive neural network hybrid method for job-shop scheduling problems. Control and Decision, 13(Supplement), pp.402-407.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)