Warehouse Vehicle Routing using Deep Reinforcement Learning
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-trainin...
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| Format: | Others |
| Language: | English |
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Uppsala universitet, Institutionen för informationsteknologi
2019
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396853 |
| Summary: | 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. |
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