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 ac-cumulated a large amount of data for the manufacturing system.Meanwhile,advanced methods such as artificial in-telligence provide effective means for data analysis and real-time control of shop floor.Therefore,a production-data-based dynamic scheduling method was proposed to minimize the makespan for the hybrid flow shop scheduling prob-lem 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,ReliefF algorithm was adopted to filter re-dundant 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 pro-posed method had good feature selection ability and high prediction accuracy.Compared with other real-time schedu-ling methods,it had better performance,and could effectively guide the manufacturing execution process according to the real-time state of shop floor.