Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (10): 63-68.doi: 10.16180/j.cnki.issn1007-7820.2021.10.010

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An Improve Particle Swarm Optimization Algorithm for Distribution and Flexible Job-Shop Scheduling Problem

CHEN Qiang,WANG Yujia,LIN Weixing,CHEN Wanfen   

  1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science, Shanghai 201620,China
  • Received:2020-06-07 Online:2021-10-15 Published:2021-10-18
  • Supported by:
    National Natural Science Foundation of China(61403249)


An improved particle swarm optimization is proposed in this study for the complex distribution and flexible job-shop scheduling problem. According to the characteristics of the problem, this study proposes a two-layer particle encoding and decoding method, so that the particle swarm algorithm can effectively solve the discrete optimization problem. Additionally, an improved crowded distance is used to select high-quality candidate solutions from many non-dominated solutions. In the iterative process, the task assignment strategy is used to balance the exploration and exploitation of particles. Finally, in the production modes of 2 factories and 3 factories, the performance of the algorithm is evaluated by 20 sets of distribution and flexible job-shop scheduling test cases. Experimental results show that the proposed algorithm can effectively solve distribution and flexible job-shop scheduling problem, and can obtain a better scheduling scheme in 11 sets of examples.

Key words: distribution and flexible job-shop scheduling problem, crowded distance, task assignment strategy, encoding, decoding, particle swarm algorithm, non-dominated solutions, discrete

CLC Number: 

  • TP18