发表机构
Moscow State University; Space Research Institute of RAS（莫斯科国立大学; 俄罗斯科学院空间研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出JAMPR+/L2D神经启发式方法，用于动态环境下带约束车辆路径问题，在CVRPLIB基准上超过85%实例优于HGS，并通过快速微调适应距离矩阵变化，兼顾精度与推理速度。
AI 中文摘要
具有现实世界约束的车辆路径问题（我们考虑车辆容量限制、时间窗约束、取送货多车场——CPDPTW）带来了显著的计算挑战。虽然经典的精确方法和启发式方法在解决中小规模问题（$N\lesssim100$）时仍然有效，但它们往往缺乏针对更大规模物流任务的适应性和可扩展性。在这项工作中，我们展示了为解决大规模CPDPTW问题而提出的JAMPR+/L2D强化学习深度学习模型，如何在图距离矩阵发生显著变化的情况下被采用。我们在CVRPLIB基准上测试了JAMPR+/L2D模型在中等规模CVRP和VRPTW问题上的性能：JAMPR+/L2D在超过85%的实例中优于最先进的启发式方法HGS，实现了目标间隙的改进。我们表明，在CPDPTW问题上训练的JAMPR+/L2D模型，对于具有更简单约束（CVRP、VRPTW）的任务、不同的问题规模以及距离矩阵的适度变化，都能很好地泛化。对于距离矩阵的更显著变化，我们在此提出对JAMPR+进行快速微调：在ORTEC数据（针对CPDPTW）上，所提出的策略显著减少了目标间隙，而无需完全重新训练模型，这将在距离矩阵变化的实际路由场景中同时提供模型的准确性和快速推理能力。
英文摘要
The vehicle routing problems with real-world constraints (we consider vehicles capacity limits, time windows constrains, pickup-and-delivery multi-depo --- CPDPTW) pose significant computational challenges. While classical exact and heuristic methods remain effective to solve problems of small/medium size ($N\lesssim100$), they often lack adaptability and scalability for larger logistics tasks. In this work, we show how JAMPR+/L2D RL deep learning model, proposed in to solve large CPDPTW problems can be adopted in the case of substantial changes of graph distance matrix. We test performance of JAMPR+/L2D model for medium-sized CVRP and VRPTW problems on CVRPLIB benchmarks: JAMPR+/L2D outperforms the state-of-the-art heuristic HGS in over 85\% of instances, achieving improvement in objective gap. We show that the JAMPR+/L2D model trained on CPDPTW problem, generalizes well for tasks with simpler constraints (CVRP, VRPTW), for different problem sizes and for moderate changes in distance matrixes. For more substantial changes in distance matrixes, we propose here to make fast finetuning of JAMPR+: on ORTEC data (for CPDPTW) the proposed strategy remarkably reduces the objective gap without full model retraining, what will give both accuracy and rapid inference of the model in the practical routing scenarios with distance matrix changes.
CommentsAutomation and Remote Control accepted
