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Mathematical Problems in Engineering
Volume 2012, Article ID 478981, 27 pages
Research Article

A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems

1Department of Management, College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China
2School of Software, Shenzhen Institute of Information Technology, Shenzhen 518029, China

Received 24 March 2012; Revised 24 May 2012; Accepted 25 May 2012

Academic Editor: Alex Elias-Zuniga

Copyright © 2012 Jian Xiong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper addresses multiobjective flexible job-shop scheduling problem (FJSP) with three simultaneously considered objectives: minimizing makespan, minimizing total workload, and minimizing maximal workload. A hybrid multiobjective evolutionary approach (H-MOEA) is developed to solve the problem. According to the characteristic of FJSP, a modified crowding distance measure is introduced to maintain the diversity of individuals. In the proposed H-MOEA, well-designed chromosome representation and genetic operators are developed for FJSP. Moreover, a local search procedure based on critical path theory is incorporated in H-MOEA to improve the convergence ability of the algorithm. Experiment results on several well-known benchmark instances demonstrate the efficiency and stability of the proposed algorithm. The comparison with other recently published approaches validates that H-MOEA can obtain Pareto-optimal solutions with better quality and/or diversity.