Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs


This paper presents a simulated genetic algorithm (GA) model of scheduling the flow shop problem with re-entrant jobs. The objective of this research is to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines in the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs reenter to the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the current industrial practices.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: © Springer-Verlag London Limited 2011. The final publication is available at Springer via
Uncontrolled Keywords: bi-objective,flow shop,genetic algorithm,re-entrant ,Industrial and Manufacturing Engineering,Control and Systems Engineering,Computer Science Applications,Software,Mechanical Engineering
Publication ISSN: 1433-3015
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://link.spr ... 0170-011-3251-4 (Publisher URL)
PURE Output Type: Article
Published Date: 2011-10-01
Published Online Date: 2011-04-05
Authors: Lee, Carman K.M.
Lin, Danping
Ho, William
Wu, Zhang



Version: Accepted Version

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