| Title: | Reinforcement learning relocation assignment in the multiple-deep storage system |
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| Authors: | ID Marolt, Jakob (Author)
ID Rosi, Bojan (Author)
ID Lerher, Tone (Author) |
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| Files: |  Marolt,_Rosi,_Lerher_-_Reinforcement_learning_relocation.pdf (659,02 KB)
MD5: 48380396E5ADEB3BDC3353F148919BFC
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.08 - Published Scientific Conference Contribution |
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| Organization: | FL - Faculty of Logistic
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| Abstract: | 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 |
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| Keywords: | relocation problem, reinforcement learning, DQN, multiple-deep, storage system |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 23.09.2022 |
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| Year of publishing: | 2022 |
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| Number of pages: | Str. 93-96 |
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| PID: | 20.500.12556/DKUM-83934  |
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| UDC: | 004.85:164 |
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| COBISS.SI-ID: | 126076419 |
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| Note: |
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| Publication date in DKUM: | 14.03.2023 |
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| Views: | 767 |
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| Downloads: | 22 |
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| Metadata: |    |
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| Categories: | Misc.
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