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The adoption of reinforcement learning in the logistics industry: A case study at a large international retailer

Gemmink, M.W.T. (2019) The adoption of reinforcement learning in the logistics industry: A case study at a large international retailer.

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Abstract:Whereas supervised and unsupervised learning have already reached widespread adoption within the logistics industry, reinforcement learning remains largely uncharted territory. Reinforcement learning is particularly interesting as agents are able to learn based on experience. Applications of the technique so far focused primarily on games but reinforcement learning could also be implemented within the business processes of logistic organisations. Because no clear and concise model for reinforcement learning adoption exists, this thesis is aimed at creating one. Conducting exploratory research and a literature review formed the basis for the model. A reinforcement learning agent is designed that is able to solve (a part of) the product allocation problem within the warehouses of Albert Heijn, also called slotting. The agent successfully learned how to allocate products according to the requirements prioritised by the company. Using intelligence amplification, operational employees are able to use the agent and increase their performance. The model was validated using expert opinions and the performance of the agent gives logistic organisations an idea about whether or not and how to use reinforcement learning in their business processes.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science, 85 business administration, organizational science
Programme:Business Information Technology MSc (60025)
Link to this item:http://purl.utwente.nl/essays/80122
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