Reinforcement learning and digital twin-based real-time scheduling method in intelligent manufacturing systems

Abstract

Optimization efficiency and decision-making responsiveness are two conflicting objectives to be considered in intelligent manufacturing. Therefore, we proposed a reinforcement learning and digital twin-based real-time scheduling method, called twins learning, to satisfy multiple objectives simultaneously. First, the interaction of multiple resources is constructed in a virtual twin, including physics, behaviors, and rules to support the decision-making. Then, the real-time scheduling problems are modeled as Markov Decision Process and reinforcement learning algorithms are developed to learn better scheduling policies. The case study indicates the proposed method has excellent adaptability and learning capacity in intelligent manufacturing.