Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows

2024-02-15 · Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac

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.

📄 PDF Abstract BibTeX arXiv:2402.09765

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reinforcement-learning Reinforcement Learning

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