Cluster Computing

, Volume 22, Supplement 6, pp 14767–14775 | Cite as

Supply chain scheduling optimization based on genetic particle swarm optimization algorithm

  • Feng Xiong
  • Peisong Gong
  • P. Jin
  • J. F. FanEmail author


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.


Scheduling optimization Hybrid algorithm Genetic algorithm Particle swarm algorithm 



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


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


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Business AdministrationZhongnan University of Economics and LawWuhanChina
  2. 2.IOPSE, Institute of Operation Management & System EngineeringZhongnan University of Economics and LawWuhanChina
  3. 3.Zhongnan University of Economics and LawWuhanChina
  4. 4.School of Civil Engineering and ArchitectureWuhan University of TechnologyWuhanChina

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