Smart manufacturing is an important research field that is associated with production planning and scheduling, the Internet of Things and artificial intelligence technologies. Production lines use advanced planning and scheduling systems for production operations, time forecasting and planning; integrated manufacturing execution systems are used to collect real-time production information via the Internet of Things to strengthen scheduling control; and artificial intelligence machine learning technology is used to perform predictive maintenance to achieve high-accuracy planning and scheduling. Advanced planning and scheduling systems use genetic algorithms for planning with the aim of increasing speed and accuracy, and the integration of realtime production information from manufacturing execution systems and dynamic adjustments to shift planning are important issues in smart manufacturing. A traditional cyberphysical system integrates historical and realtime production information and carries out a machine learning analysis to improve the production scheduling efficiency, but the prediction of production times for new product orders is a topic that needs further research. This paper proposes new methods of dynamic productivity prediction and new production feature selection, with the aim of improving the performance of advanced planning and scheduling systems. A genetic ant colony algorithm is used to predict dynamic productivity based on realtime production information, to reduce the error between production time plans and actual operations. Historical production information is analysed, and the best correlation coefficient is used in new production feature selection, in order to reduce the discrepancy between production productivity forecasts and actual results. Our proposed dynamic productivity prediction method can reduce the error by at least 1.5% compared with other schemes in the literature, while the proposed production feature selection method can reduce the error by 0.08%.