Genetic algorithm and adaptive neural network hybrid method for job-shop scheduling problems
Date
1998-07
Authors
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.