SCHEDULING OPTIMIZATION OF A WHEEL HUB PRODUCTION LINE BASED ON FLEXIBLE SCHEDULING

Authors

  • Yongfeng Dong 1School of Artificial Intelligence Hebei University of Technology Tianjin, China
  • Yurong Jin Hebei University of Technology http://orcid.org/0000-0003-2020-6738
  • Zhiguang Li CITIC Dicastal Co., Ltd. Heibei, China
  • Haipeng Ji 3Tianjin JingNuo Data Technology Co., Ltd. Tianjin, China 4School of Mechanical Engineering Hebei University of Technology Tianjin, China
  • Jing Liu 1School of Artificial Intelligence Hebei University of Technology Tianjin, China 3Tianjin JingNuo Data Technology Co., Ltd. Tianjin, China

DOI:

https://doi.org/10.23055/ijietap.2020.27.5.6133

Abstract

Flexible production scheduling is a problem for the field of intelligent manufacturing. Generally, methods for solving flexible job-shop scheduling include tabu search, simulated annealing, and particle swarm optimization. However, most algorithms cannot be directly used to solve actual production scheduling problems because of their efficiency and quality problems. This paper proposes a particle swarm-immunization algorithm based on the bottleneck process. First, the particle swarm optimization is optimized by the time decomposition method and workpiece urgency parameter; it is then embedded in an artificial immune algorithm after the process and mathematical model structure decomposition. Next, a multiobjective optimization mathematical model is established as the concentration adjustment mechanism of the artificial immune algorithm, and the method of matching the highest efficiency process to the bottleneck process is used to improve the variation function. Finally, through empirical research on wheel hub production data analysis, our method can reduce production time and energy consumption.

Author Biography

Yurong Jin, Hebei University of Technology

School of Artificial Intelligence

Published

2021-04-29

How to Cite

Dong, Y., Jin, Y., Li, Z., Ji, H., & Liu, J. (2021). SCHEDULING OPTIMIZATION OF A WHEEL HUB PRODUCTION LINE BASED ON FLEXIBLE SCHEDULING. International Journal of Industrial Engineering: Theory, Applications and Practice, 27(5). https://doi.org/10.23055/ijietap.2020.27.5.6133

Issue

Section

Special Issue on Data-driven Computational Intelligence in Industries Application