A robust scheduling rule using a Neural Network in dynamically changing job-shop environments. (31st March 2008)

• Record Type: Journal Article
• Authors: Eguchi, Toru; Oba, Fuminori; Toyooka, Satoru
• Is Part Of: International journal of manufacturing technology and management. Volume 14:Number 3/4(2008)
• Page Start: 266
• Page End: 288
• Publication Date: 2008-03-31
• Subjects: job shop scheduling -- dynamic scheduling -- neural networks -- ANNs -- dispatching rules -- priority rules -- tardiness cost; Manufacturing processes -- Periodicals; Manufacturing processes -- Automation -- Periodicals; Production management -- Periodicals; 670.427068
• Languages: English
• ISSNs: 1368-2148

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.