<![CDATA[This research explores the integration of Graph Neural Networks (GNNs) and Reinforcement Learning (RL) for dynamic yield optimization and resource allocation in industrial systems. We present a numerical example involving a small manufacturing setup with three machines, where GNNs are employed to model complex interactions and derive meaningful embeddings of machine states. These embeddings are then used to predict yield and cost through linear combination functions. RL is utilized to optimize resource allocation dynamically, balancing yield and cost through a carefully designed reward function. The results demonstrate the effectiveness of GNNs in capturing machine interactions and the adaptability of RL in optimizing operational parameters in real-time. This combined approach showcases significant potential for enhancing efficiency, cost-effectiveness, and overall performance in various industrial applications, providing a robust framework for continuous improvement and adaptive decision-making in dynamic environments.]]>
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