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A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility

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Abstract

In this paper, a novel hybrid discrete particle swarm optimization algorithm is proposed to solve the dual-resource constrained job shop scheduling problem with resource flexibility. Particles are represented based on a three-dimension chromosome coding scheme of operation sequence and resources allocation. Firstly, a mixed population initialization method is used for the particles. Then a discrete particle swarm optimization is designed as the global search process by taking the dual-resources feature into account. Moreover, an improved simulated annealing with variable neighborhoods structure is introduced to improve the local searching ability for the proposed algorithm. Finally, experimental results are given to show the effectiveness of the proposed algorithm.

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Acknowledgments

This project is supported by National Natural Science Foundation of China (NSFC Grant No. 61379123) and the National Key Technology R&D Program in the 12th Five Year Plan of China (Grant No. 2012BAD10B01).

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Authors and Affiliations

  1. Department of Computer and Information Technology, Zhejiang Police College, Hangzhou, People’s Republic of China

    Jing Zhang

  2. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, People’s Republic of China

    Wanliang Wang & Xinli Xu

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  1. Jing Zhang
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  2. Wanliang Wang
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  3. Xinli Xu
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Correspondence to Jing Zhang.

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Zhang, J., Wang, W. & Xu, X. A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility. J Intell Manuf 28, 1961–1972 (2017). https://doi.org/10.1007/s10845-015-1082-0

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