Adopting reinforcement learning in operational spare part management : visualizing the black box of decision-making

This thesis researches the benefits of reinforcement learning in operational spare part management. Because reinforcement learning is not often used in a (semi) closed-loop inventory environment, an extensive literature study is performed first. Next, a proof of concept shows a reinforcement learning method's performance compared to the exact solution and a simple greedy heuristic.