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.

通过强化学习方法优化随机车辆路径问题，减少货物运输中的行程成本，提出一种新的模型，用于考虑不确定的行程成本和需求，以及确定的客户时间窗口，通过基于注意力的神经网络进行训练，实现路径成本的最小化，并超越基于蚁群算法的模型，在行程成本上减少了 1.73%，该模型独特地整合了外部信息，展现了在不同环境中的稳健性，对未来随机车辆路径问题研究和产业应用具有价值。