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.

作为电信供应商，公司的关键任务是在停电期间维持电信服务。为了实现该任务，本文提出了一种解决方案，即通过电动汽车直接为基站提供电力。我们将这个问题定义为Electric Vehicle Routing Problem (EVRP)的一个新变体，并提出了一种解决方法，该方法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。我们的求解器在合成数据集和真实数据集上进行了评估，结果表明它在客观值和计算时间方面优于基准线。此外，我们还分析了我们的求解器的泛化能力和可扩展性，证明了它在未知设置和大规模问题上的能力。

应急电源供应的电动车辆路径规划：面向电信基站救援