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Warehouse Vehicle Routing using Deep Reinforcement Learning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this study a Deep Reinforcement Learning algorithm, MCTS-CNN, is applied on the Vehicle Routing Problem (VRP) in warehouses. Results in a simulated environment show that a Convolutional Neural Network (CNN) can be pre-trained on VRP transition state features and then effectively used post-training within Monte Carlo Tree Search (MCTS). When pre-training works well enough better results on warehouse VRP’s were often obtained than by a state of the art VRP Two-Phase algorithm. Although there are a number of issues that render current deployment pre-mature in two real warehouse environments MCTS-CNN shows high potential because of its strong scalability characteristics.

Place, publisher, year, edition, pages
2019. , p. 73
Series
IT ; 19053
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-396853OAI: oai:DiVA.org:uu-396853DiVA, id: diva2:1369370
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-11Bibliographically approved

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fulltext(5092 kB)33 downloads
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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf