A robust scheduling rule using a Neural Network in dynamically changing job-shop environments

Toru Eguchi, Fuminori Oba and Satoru Toyooka

International Journal of Manufacturing Technology and Management, 2008, vol. 14, issue 3/4, 266-288

Abstract: Scheduling plays a critical role in job-shops that produce a wide variety of different jobs. This paper presents a robust and effective scheduling rule using a Neural Network (NN) trained as a priority rule for these complex and dynamic job-shops. The training is efficiently carried out through two stages: the first for effective scheduling under specific scheduling conditions, and the second for robust scheduling under various scheduling conditions. Numerical experiments under various scheduling conditions in which the level of machine utilisation and due-date tightness dynamically changes show that a trained NN outperforms the best dispatching rules available in the literature.

Keywords: job shop scheduling; dynamic scheduling; neural networks; ANNs; dispatching rules; priority rules; tardiness cost. (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=17727 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmtma:v:14:y:2008:i:3/4:p:266-288

Access Statistics for this article

More articles in International Journal of Manufacturing Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().