Abstract
Based on the basic particle swarm optimization algorithm and the analysis of the basic principles of job shop scheduling problem, an improved discrete particle swarm optimization (IDPSO) is proposed in the paper. In the IDPSO, the discrete particle location is updated by the operation of mutation and crossover based on the evolutionary mechanism of PSO, and a neighbourhood algorithm is introduced to enhance the ability of local search. Finally a simulation results of an example proved the feasibility and validity of the algorithm.
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Acknowledgments
The authors thank the Academic Committee Chairs Prof. Ershi Qi and the anonymous reviewers for their constructive comments to improve the paper. This research is partially supported by the National Science Foundation of China (No. 51375004), and the ministry of education of humanit ies and social science project (No.14YJA630079), and the Doctoral Scientific Fund Project of Hubei University of Automotive Technology (No. BK201408, No. BK201301), and the scientific research project of Hubei province education office (No. Q20131801).
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Yin, L., Yang, L., Hu, M. (2015). Job Shop Scheduling Based on Improved Discrete Particle Swarm Optimization. In: Qi, E., Shen, J., Dou, R. (eds) Proceedings of the 21st International Conference on Industrial Engineering and Engineering Management 2014. Proceedings of the International Conference on Industrial Engineering and Engineering Management. Atlantis Press, Paris. https://doi.org/10.2991/978-94-6239-102-4_21
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DOI: https://doi.org/10.2991/978-94-6239-102-4_21
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