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Supply chain scheduling optimization based on genetic particle swarm optimization algorithm

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Abstract

In order to optimize supply chain scheduling problem in mass customization mode, the mathematical programming of supply chain scheduling optimization problem is modelled. At the same time, model mapping is defined as a directed graph to facilitate the application of intelligent search algorithms. In addition, the features of genetic algorithm and particle swarm algorithm are introduced. Genetic algorithm has a strong global search capability, and particle swarm optimization algorithm has fast convergence speed. Therefore, the two algorithms are combined to construct a hybrid algorithm. Finally, the hybrid algorithm is used to solve the supply chain optimization scheduling problem model. Compared with other algorithms, the results show that the hybrid algorithm has better performance. The mathematical programming model used in this paper can be further extended and improved.

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 111578109), the National Natural Science Foundation of China (Grant: 11111121005).

Funding

Funding

Funding was provided by National Natural Science Foundation Project (Grant No. 71701213).

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Authors and Affiliations

  1. School of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China

    Feng Xiong

  2. IOPSE, Institute of Operation Management & System Engineering, Zhongnan University of Economics and Law, Wuhan, 430073, China

    Feng Xiong

  3. Zhongnan University of Economics and Law, Wuhan, 430073, China

    Peisong Gong & P. Jin

  4. School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan, 430070, China

    J. F. Fan

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  1. Feng Xiong
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  2. Peisong Gong
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  3. P. Jin
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  4. J. F. Fan
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Corresponding author

Correspondence to J. F. Fan.

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Xiong, F., Gong, P., Jin, P. et al. Supply chain scheduling optimization based on genetic particle swarm optimization algorithm. Cluster Comput 22 (Suppl 6), 14767–14775 (2019). https://doi.org/10.1007/s10586-018-2400-z

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  • DOI: https://doi.org/10.1007/s10586-018-2400-z

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