POMO+利用起点信息提升车辆路径问题求解效率与精度
POMO+: Leveraging starting nodes in POMO for solving Capacitated Vehicle Routing Problem
- 引入起始节点信息优化路径搜索策略
- 在100客户以内实例上收敛更快且结果更优
- 适合需要高效求解车辆路径问题的研究者
近年来，强化学习方法在求解组合优化问题方面展现出巨大潜力。其中，基于强化学习的POMO模型在多种车辆路径问题（VRP）变体中表现优异。然而仍有改进空间。本文提出POMO+，通过利用起始节点信息，使求解过程更具方向性。我们在CVRPLIB数据集上进行实验，发现该方法在客户数不超过100的问题实例中，收敛速度更快，解的质量更高。本研究有望推动该领域进一步发展。
原文摘要 · Abstract (English)
In recent years, reinforcement learning (RL) methods have emerged as a promising approach for solving combinatorial problems. Among RL-based models, POMO has demonstrated strong performance on a variety of tasks, including variants of the Vehicle Routing Problem (VRP). However, there is room for improvement for these tasks. In this work, we improved POMO, creating a method (\textbf{POMO+}) that leverages the initial nodes to find a solution in a more informed way. We ran experiments on our new model and observed that our solution converges faster and achieves better results. We validated our models on the CVRPLIB dataset and noticed improvements in problem instances with up to 100 customers. We hope that our research in this project can lead to further advancements in the field.
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