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A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem

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Abstract

This research proposes a revised discrete particle swarm optimization (RDPSO) to solve the permutation flow-shop scheduling problem with the objective of minimizing makespan (PFSP-makespan). The candidate problem is one of the most studied NP-complete scheduling problems. RDPSO proposes new particle swarm learning strategies to thoroughly study how to properly apply the global best solution and the personal best solution to guide the search of RDPSO. A new filtered local search is developed to filter the solution regions that have been reviewed and guide the search to new solution regions in order to keep the search from premature convergence. Computational experiments on Taillard’s benchmark problem sets demonstrate that RDPSO significantly outperforms all the existing PSO algorithms.

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Acknowledgments

This paper was supported in part by the National Science Council, Taiwan, ROC, under the contract NSC101-2221-E-004-004. The authors are grateful to the anonymous referees for their constructive comments that have greatly improved the presentation of this paper.

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Authors and Affiliations

  1. Department of Accounting Information, Takming University of Science and Technology, Taipei 114, Taiwan, Republic of China

    Chun-Lung Chen

  2. Research Center for Information Technology Innovation, Academia Sinica, Taipei 115, Taiwan, Republic of China

    Shin-Ying Huang

  3. Department of Computer Science and Information Engineering, Chinese Culture University, Taipei 111, Taiwan, Republic of China

    Yeu-Ruey Tzeng

  4. Department of Management Information Systems, National Chengchi University, Taipei 116, Taiwan, Republic of China

    Chuen-Lung Chen

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  1. Chun-Lung Chen
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  2. Shin-Ying Huang
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  3. Yeu-Ruey Tzeng
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  4. Chuen-Lung Chen
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Correspondence to Chun-Lung Chen.

Additional information

Communicated by F. Marcelloni.

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Chen, CL., Huang, SY., Tzeng, YR. et al. A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem. Soft Comput 18, 2271–2282 (2014). https://doi.org/10.1007/s00500-013-1199-z

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