Agent-based approach integrating deep reinforcement learning and hybrid genetic algorithm for dynamic scheduling for Industry 3.5 smart production

Dynamic scheduling is crucial for semiconductor manufacturing as product-mix is increasing with shortening product life cycle. However, the present problem is challenging owing to complicated constraints and short time for decision making. Focusing on realistic needs, this research aims to develop a novel agent-based approach that integrates deep reinforcement learning and hybrid genetic algorithm for the unrelated parallel machine scheduling problem with sequence-dependent setup time. In particular, deep Q network (DQN), a combination of deep learning and Q learning, is employed to train a scheduling agent. A trained agent could perform job allocation tasks in short computation time for addressing the dynamic scheduling problem. Furthermore, the proposed hybrid genetic algorithm is employed to enhance searching effectiveness and efficiency during the training process. To estimate the validity, scenarios are designed to compare the developed solution with a number of dispatching rules and other knowledge-based approaches. The experimental results have shown practical viability of the developed solution. Indeed, the developed solution is implemented in a semiconductor manufacturing company.