Human-Machine Interactive Learning Method Based on Active Learning for Smart Workshop Dynamic Scheduling

In the field of dynamic scheduling, workers and scheduling models (SMs) play a crucial role in decision-making. Workers are able to help SM training by sample labeling, thereby enhancing the decision-making ability of SMs. However, existing supervised learning methods require a large number of labeled samples to train SMs, which limits the learning efficiency between workers and SMs. In this article, a human-machine interactive learning method based on active learning (HMILM/AL) is proposed. The method introduces active learning (AL) techniques to reduce labeling costs and improve learning efficiency. Referring to the AL framework, only a small subset of samples are selected from an unlabeled dataset and are labeled by workers, to train SMs. To further reduce labeling costs, sample selection, the key to the HMILM/AL, is improved by two strategies. First, a novel hybrid selection strategy (NHSS) is developed. By identifying and selecting more useful samples in an unlabeled dataset, the NHSS promotes efficient use of workers, and reduces labeling costs. Second, an enhanced NHSS (E-NHSS) is proposed, which considers both the difficulty of labeling samples and the usefulness of the samples. It reduces labeling costs by selecting easily labeled samples as much as possible. Finally, the proposed method is evaluated through experiments conducted in a real smart workshop. The results demonstrate that the HMILM/AL is very competitive compared with existing supervised learning methods. Moreover, both the NHSS and the E-NHSS can reduce labeling costs efficiently.