An improved constraint satisfaction adaptive neural network for job-shop scheduling.

Date

2010

Advisors

Journal Title

Journal ISSN

ISSN

1094-6136

Volume Title

Publisher

Springer-Verlag

Type

Article

Peer reviewed

Yes

Abstract

This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.

Description

Keywords

Job-shop scheduling, Constraint satisfaction adaptive neural network, Heuristics, Active schedule, Non-delay schedule, Priority rule, Computational complexity

Citation

Yang, S. et al. (2010) An improved constraint satisfaction adaptive neural network for job-shop scheduling. Journal of Scheduling, 13(1), February 2010, pp. 17-38.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)