By Topic

Genetic algorithm based multi-objective scheduling in a flow shop with batch processing machines

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

5 Author(s)
Deming Lei ; School of Automation, Wuhan University of Technology, University of Springfield, Hubei Province, China ; Qiongfang Zhang ; Wen Cheng ; Tao Wang
more authors

In this paper, the problem of minimizing makespan and the total tardiness in a flow shop with batch processing machines (BPM) is considered and an efficient genetic algorithm (GA) is presented, in which job permutation is the only optimization object and the solution of problem can be directly obtained using the permutation. To obtain a set of non-dominated solutions, a rank and the weighted objective based binary tournament selection and an external archive updating strategy are also adopted. The proposed GA is finally tested and the computational results show its promising performance on multi-objective scheduling of flow shop with BPM.

Published in:

Intelligent Control and Automation (WCICA), 2010 8th World Congress on

Date of Conference:

7-9 July 2010