mlsanityData to 14 Oct 2026

Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows

arXiv 2402.09765 ↗First posted 15 Feb 2024cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 36 references that are in the map. First posted on arXiv 15 Feb 2024.

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6 of 36 references.

References · 6

  1. 2018Attention, Learn to Solve Routing Problems!cited by 656
  2. 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 608
  3. 2018Reinforcement Learning for Solving the Vehicle Routing Problemcited by 333
  4. 2019An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problemcited by 269
  5. 2021Learning to Delegate for Large-scale Vehicle Routingcited by 109
  6. 2022Solving Large-Scale Dynamic Vehicle Routing Problems with Stochastic Requestscited by 3

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  5. 2026A Deep Reinforcement Learning Algorithm for the Vehicle Routing Problem with Stochastic Demands and Outsourcing
  6. 2026Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry
  7. 2026Demand Acceptance using Reinforcement Learning for Dynamic Vehicle Routing Problem with Emission Quota
  8. 2026A Curriculum-Based Deep Reinforcement Learning Framework for the Electric Vehicle Routing Problem