面向应急供电的电动汽车路径问题：迈向电信基站救援
Electric Vehicle Routing Problem for Emergency Power Supply: Towards Telecom Base Station Relief
AI总结：
本文针对停电时电信基站应急供电问题，提出一种结合规则车辆选择与强化学习节点选择的电动汽车路径求解器，在合成与真实数据上验证了其优于基线并具备良好泛化与扩展能力。
摘要：
作为一家电信运营商，我们公司肩负着一项关键使命：即使在停电期间也要维持电信服务。为完成这一使命，必须维持电信基站的供电。在此，我们考虑一种解决方案，即电动汽车（EV）通过行驶至基站所在位置直接向其供电。目标是找到能够同时最小化所有电动汽车总行驶距离和断电站点数量的电动汽车路径。在本文中，我们将该路径问题形式化为电动汽车路径问题（EVRP）的一种新变体，并提出一种求解器，该求解器结合了基于规则的车辆选择器和基于强化学习（RL）的节点选择器。车辆选择器的规则确保了所选电动汽车开始移动时环境状态的精确性。此外，基于强化学习模型的节点选择能够实现快速路径生成，这在紧急情况下至关重要。我们在合成数据集和真实数据集上对求解器进行了评估。结果表明，我们的求解器在目标值和计算时间方面均优于基线方法。此外，我们还分析了求解器的泛化能力和可扩展性，展示了其应对未见设置和大规模问题的能力。另请查看我们的项目页面：https://ntt-dkiku.github.io/rl-evrpeps。
英文摘要：
As a telecom provider, our company has a critical mission to maintain telecom services even during power outages. To accomplish the mission, it is essential to maintain the power of the telecom base stations. Here we consider a solution where electric vehicles (EVs) directly supply power to base stations by traveling to their locations. The goal is to find EV routes that minimize both the total travel distance of all EVs and the number of downed base stations. In this paper, we formulate this routing problem as a new variant of the Electric Vehicle Routing Problem (EVRP) and propose a solver that combines a rule-based vehicle selector and a reinforcement learning (RL)-based node selector. The rule of the vehicle selector ensures the exact environmental states when the selected EV starts to move. In addition, the node selection by the RL model enables fast route generation, which is critical in emergencies. We evaluate our solver on both synthetic datasets and real datasets. The results show that our solver outperforms baselines in terms of the objective value and computation time. Moreover, we analyze the generalization and scalability of our solver, demonstrating the capability toward unseen settings and large-scale problems. Check also our project page: https://ntt-dkiku.github.io/rl-evrpeps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
