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The Scientific World Journal
Volume 2014, Article ID 596850, 11 pages
Research Article

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

1State Key Laboratory of Synthetic Automation for Process Industries, Northeastern University, Shenyang 110819, China
2College of Computer Science, Liaocheng University, Liaocheng 252059, China

Received 21 February 2014; Accepted 30 March 2014; Published 29 April 2014

Academic Editors: P. Agarwal, V. Bhatnagar, and Y. Zhang

Copyright © 2014 Jun-qing Li 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.


A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron’s benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.