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Mathematical Problems in Engineering
Volume 2017, Article ID 3630869, 9 pages
Research Article

Synergy of Genetic Algorithm with Extensive Neighborhood Search for the Permutation Flowshop Scheduling Problem

1Department of Distribution Management, National Taichung University of Science and Technology, Taichung 404, Taiwan
2Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung 404, Taiwan

Correspondence should be addressed to Chien-Che Huang; moc.liamg@8290selrahc

Received 19 August 2016; Revised 24 December 2016; Accepted 17 January 2017; Published 13 February 2017

Academic Editor: Jean J. Loiseau

Copyright © 2017 Rong-Chang Chen 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.


The permutation flowshop scheduling problem (PFSP) is an important issue in the manufacturing industry. The objective of this study is to minimize the total completion time of scheduling for minimum makespan. Although the hybrid genetic algorithms are popular for resolving PFSP, their local search methods were compromised by the local optimum which has poorer solutions. This study proposed a new hybrid genetic algorithm for PFSP which makes use of the extensive neighborhood search method. For evaluating the performance, results of this study were compared against other state-of-the-art hybrid genetic algorithms. The comparisons showed that the proposed algorithm outperformed the other algorithms. A significant 50% test instances achieved the known optimal solutions. The proposed algorithm is simple and easy to implement. It can be extended easily to apply to similar combinatorial optimization problems.