By Topic

Introduction to the cluster on “machine learning approaches to scheduling”

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)

Scheduling jobs in complex manufacturing environments is an exceedingly challenging task. Studies have shown that dispatchers often rotate out of such positions within two years. Even seasoned dispatchers are unable to distill their knowledge in any meaningful way. Four articles in this issue are devoted to “machine learning approaches to scheduling.” They were presented at a workshop conducted and sponsored by the Decision and Information Sciences Department of the College of Business Administration at the University of Florida. A survey is provided by Aytug, Battacharyya, Koehler, and Snowdon to generally acquaint the practitioner with machine learning in scheduling. Piramuthu, Raman, and Shaw present an adaptive learning system for scheduling circuit board assembly. Chaturvedi and Nazareth consider scheduling problems requiring learning of conditional levels of knowledge. Finally, Chaturvedi, Choubey, and Roan present a machine learning method that seeks to find time invariant and time sensitive knowledge

Published in:

IEEE Transactions on Engineering Management  (Volume:41 ,  Issue: 2 )