Cart (Loading....) | Create Account
Close category search window

Scaling Adaptive Agent-Based Reactive Job-Shop Scheduling to Large-Scale Problems

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
$31 $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

2 Author(s)
Gabel, T. ; Dept. of Math. & Comput. Sci., Inst. of Cognitive Sci. Univ. of Osnabruck ; Riedmiller, M.

Most approaches to tackle job-shop scheduling problems assume complete task knowledge and search for a centralized solution. In this work, we adopt an alternative view on scheduling problems where each resource is equipped with an adaptive agent that, independent of other agents, makes job dispatching decisions based on its local view on the plant and employs reinforcement learning to improve its dispatching strategy. We delineate which extensions are necessary to render this learning approach applicable to job-shop scheduling problems of current standards of difficulty and present results of an adequate empirical evaluation

Published in:

Computational Intelligence in Scheduling, 2007. SCIS '07. IEEE Symposium on

Date of Conference:

1-5 April 2007

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.