Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929244312961024 |
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| author | Iklassov, Zangir Sobirov, Ikboljon Solozabal, Ruben Takac, Martin |
| author_facet | Iklassov, Zangir Sobirov, Ikboljon Solozabal, Ruben Takac, Martin |
| contents | This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods delivery. We develop a novel SVRP formulation that accounts for uncertain travel costs and demands, alongside specific customer time windows. An attention-based neural network trained through reinforcement learning is employed to minimize routing costs. Our approach addresses a gap in SVRP research, which traditionally relies on heuristic methods, by leveraging machine learning. The model outperforms the Ant-Colony Optimization algorithm, achieving a 1.73% reduction in travel costs. It uniquely integrates external information, demonstrating robustness in diverse environments, making it a valuable benchmark for future SVRP studies and industry application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09765 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows Iklassov, Zangir Sobirov, Ikboljon Solozabal, Ruben Takac, Martin Artificial Intelligence This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods delivery. We develop a novel SVRP formulation that accounts for uncertain travel costs and demands, alongside specific customer time windows. An attention-based neural network trained through reinforcement learning is employed to minimize routing costs. Our approach addresses a gap in SVRP research, which traditionally relies on heuristic methods, by leveraging machine learning. The model outperforms the Ant-Colony Optimization algorithm, achieving a 1.73% reduction in travel costs. It uniquely integrates external information, demonstrating robustness in diverse environments, making it a valuable benchmark for future SVRP studies and industry application. |
| title | Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2402.09765 |