Applying multi-population genetic algorithm to the dynamic flexible job shop scheduling problem

Considering the scheduling factors such as mechanical failures and order changes in the actual production process, the dynamic flexible job shop scheduling problem has increasingly attracted the interest of researchers. The multi-population genetic algorithm (MPGA) increases the mutation probability of the general population, achieves rapid convergence, and has extremely strong robustness. Based on the MPGA, this paper studies the dynamic flexible job shop scheduling problem in actual production and processing workshop, and proposes a dynamic flexible scheduling system. The research results show that the dynamic flexible job shop scheduling is aimed at the minimization of the completion time; once the dynamic scheduling factors occur in the actual processing and production process, the three modules of the flexible dynamic scheduling system will cooperate with each other to complete the entire rescheduling work, thereby avoiding the conflict in the processing. The research findings have important guiding significance for promoting flexible scheduling of manufacturing workshop resources. © 2020 Editura Politechnica. All rights reserved.