Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows

• URL: http://arxiv.org/abs/2402.09765v1
• Date: Thu, 15 Feb 2024 07:35:29 GMT
• Title: Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
• Authors: Zangir Iklassov and Ikboljon Sobirov and Ruben Solozabal and Martin Takac
• Abstract summary: This paper introduces a reinforcement learning approach to optimize the Vehicle Routing Problem with Time Windows (SVRP) 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.
• Score: 0.09831489366502298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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