Throughout my PhD, see [9], I concentrated on Artificial Intelligence, with a special focus on Reinforcement Learning, in the context of production planning. Although we found that machine learning, in particular artificial neural networks are tools that can be applied in manufacturing firms [1,7], we also observed that current reinforcement learning algorithms are not suited for optimization problems arising in modern companies, especially in the context of production planning [8,9]. More precise, the widely-known algorithms are built for game-like environments, where only a sparse reward function is used, e.g. 0 reward until the end of the game. Hence, by analysing the fundamentals I developed an alternative reinforcement learning approach, overcoming the problems of the standard techniques [5,9]. This led to an algorithm that outperforms well-known standard reinforcement learning [5], where we apply it in the context of production planning [4].