Summary of Deep Reinforcement Learning For Multi-truck Vehicle Routing Problems with Multi-leg Demand Routes, by Joshua Levin et al.
Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes
by Joshua Levin, Randall Correll, Takanori Ide, Takafumi Suzuki, Takaho Saito, Alan Arai
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep reinforcement learning has been successfully applied to vehicle routing problems (VRPs), particularly when using encoder-decoder attention mechanisms. However, there remain under-researched VRP variants that require complex solutions. This paper focuses on a variant involving multiple trucks and multi-leg routing requirements. To tackle this problem, we extend existing encoder-decoder attention models to handle multiple trucks and multi-leg routing. Our approach allows for training on small-scale instances and then embedding into larger supply chains. We test our algorithm on a real-world supply chain environment from Japanese automotive parts manufacturer Aisin Corporation, achieving better performance than their previous best solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep reinforcement learning is used to solve complex problems with vehicles. Right now, it’s good at simple cases, but there are harder problems where no one has found a good answer yet. This paper looks at one of these harder problems: moving lots of things from place to place using multiple trucks and complicated routes. To make this work, we improved existing computer models that use attention mechanisms. Our new model can learn on small problems and then solve bigger ones. We tested it on a real-world problem for Japanese car parts company Aisin Corporation and found that our answer was better than theirs. |
Keywords
* Artificial intelligence * Attention * Embedding * Encoder decoder * Reinforcement learning
