Production data-based dynamic scheduling method for hybrid flow shop

In the context of intelligent manufacturing, information technologies such as the Internet of things have accumulated a large amount of data for the manufacturing system. Meanwhile, advanced methods such as artificial intelligence provide effective means for data analysis and real-time control of shop floor. Therefore, a production-data-bascd dynamic scheduling method was proposed to minimize the makespan for the hybrid flow shop scheduling problem with unrelated parallel machines. The production features and scheduling rules were extracted to complete the sample construction based on the high-quality scheduling scheme. Then, RclicfF algorithm was adopted to filter redundant production features and obtain scheduling samples for training and prediction. Moreover, the probabilistic neural network combined with whale optimization algorithm was used as the decision-making model to realize the training and prediction process based on scheduling samples. Finally, the experimental results showed that the proposed method had good feature selection ability and high prediction accuracy. Compared with other real-time scheduling methods, it had better performance, and could effectively guide the manufacturing execution process according to the real-time state of shop floor. © 2024 CIMS. All rights reserved.