20.500.12556/DKUM-83934 Reinforcement learning relocation assignment in the multiple-deep storage system The field of reinforcement learning shows promising results in recent publications for solving complex combinatorial problems. In this paper, an agent is trained to tackle the relocation problem that appears in various logistics systems. The relocation problem was formulated as an array that is accessible only from the top side. The agent has to relocate blocking SKUs and thus enable access to the SKU with the highest dispatch priority. We utilised the Deep Q-learning Network (DQN) to train the agent. Two case studies are presented – one with 1.2 ∙ 10 and another with 2 ∙ 10 possible states. The results display that the agents made a significantly better decision toward the learning process’s end than at the beginning relocation problem reinforcement learning DQN multiple-deep storage system problem relokacije sistemi za shranjevanje strojno učenje true false true Angleški jezik Slovenski jezik Neznano 2023-03-14 14:31:56 2023-03-14 14:31:56 2023-03-15 03:08:07 0000-00-00 00:00:00 2022 0 0 Str. 93-96 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2022-09-23 004.85:164 123843331 126076419 Marolt,_Rosi,_Lerher_-_Reinforcement_learning_relocation.pdf Marolt,_Rosi,_Lerher_-_Reinforcement_learning_relocation.pdf 1 48380396E5ADEB3BDC3353F148919BFC 3c5fbdeb1ed01d0c0e4204d289314038d6d1d7a6fe686a83599a9d0acf2f692e 7ffd882c-c26d-11ed-af07-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=166083 Fakulteta za logistiko 0 0 0