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Fabrication areas in semiconductor industry are considered one of the most complex production systems. This complexity is caused by the high-mix of products and end-user market-based demands in that industry. Its dynamic and challenging processing requirements affect the handling capabilities of traditional production management paradigms. In this paper, we propose an application for dispatching and resources allocation through reinforcement learning. The application is based on a discrete-event simulation model for a case study of a real semiconductor manufacturing system. The model is built using both data-driven and agent-based approaches. The model simulates the various processing aspects that are present normally in these complex systems. The model’s agents are responsible for dispatching tasks and allocation of the different system’s resources. They employ Deep-Q-Network …