Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (1): 75-82.doi: 10.6040/j.issn.1672-3961.0.2018.540

• Control Science & Engineering • Previous Articles     Next Articles

Optimization of job shop scheduling based on improved particle swarm optimization algorithm

Hongming LIU(),Hongyan ZENG,Wei ZHOU*(),Tao WANG   

  1. College of Automation Engineering, Chengdu University of Technology, Chengdu 610059, Sichuan, China
  • Received:2018-12-18 Online:2019-02-01 Published:2019-03-01
  • Contact: Wei ZHOU E-mail:1270078949@qq.com;zhouwei@cdut.edu.cn
  • Supported by:
    国家自然科学基金资助项目(11675028)

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Abstract:

An improved particle swarm optimization algorithm was proposed based on adaptive weights and chaos aiming at the job shop scheduling. A multiple constrained job shop scheduling model was built with the shortest machining time as the optimization goal. The mapping relationship between particle parameters and operation sequences was obtained by coding method based on ranked order value. The inertial coefficient and acceleration factor in the particle swarm optimization algorithm were improved based on the adaptive weights, so that the algorithm could dynamically adjust parameters based on the fitness value. The reverse learning was used to improve the quality of initial solution. Considering the problem of local optimum, some measures were used to enhance the search of algorithm, such as Lévy flight, variable neighborhood search and chaos. The results showed that improved particle swarm optimization could effectively improve utilization of particles, balance the global search and local search, avoid the premature convergence of the traditional particle swarm optimization algorithm, and get better results.

Key words: job shop scheduling, adaptive weights, chaos, Lévy flight, particle swarm optimization

CLC Number: 

  • TP391

Fig.1

Flowchart of improved particle swarm algorithm"

Table 1

Comparison of IPSO and PSO test results"

实例 问题规模 已知最优解/s IPSO PSO 最小值提升率/% 平均值提升率/%
最小值/ s 平均值/ s 寻优成功率/% 最小值/ s 平均值/ s 寻优成功率/%
FT06 6×6 55 55 55 100 56 58 0 1.79 5.17
FT10 10×10 930 975 1 027 0 1 075 1 196 0 9.30 14.13
FT20 20×5 1 165 1 206 1 222 0 1 429 1 526 0 15.6 19.92
LA01 10×5 666 666 666 100 666 709 5 0 4.58
LA06 15×5 926 926 926 100 926 938 35 0 1.28
LA11 20×5 1 222 1 222 1 222 100 1 222 1 259 15 0 2.94
LA16 10×10 945 973 1 011 0 1 043 1 118 0 9.40 9.57

Fig.2

Comparison of IPSO and PSO optimization curves"

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