基于强化学习的车联网供电电动车辆路径问题
A Reinforcement Learning Approach for Electric Vehicle Routing Problem with Vehicle-to-Grid Supply
摘要：
从可持续性和运营成本的角度来看，在最后一英里使用电动汽车（EV）具有吸引力。除了电动汽车固有的成本效率外，在电网需求高峰期间将能源销售回电网是车队运营商额外收入的潜在来源。为了实现这一目标，电动汽车必须在特定的时间点（高峰期）在特定的位置（放电点），即使在满足其向客户交付货物的核心目的。在这项工作中，我们考虑的问题，电动汽车路由与约束的负载能力;时间窗口;车辆到电网的能源供应（CEVRPTW-D），这不仅满足多个系统目标，但也有效地扩展到大的问题规模，涉及数百个客户和放电站。我们提出了QuikRouteKit，它使用强化学习（RL）进行EV路由来克服这些挑战。使用Solomon数据集，从RL的结果进行了比较，对精确配方的基础上混合整数线性规划（MILP）和遗传算法（GA）元分析。平均而言，结果表明RL比MILP和GA快24倍，同时在质量上接近最优（20%以内）。
英文摘要：
The use of electric vehicles (EV) in the last mile is appealing from both sustainability and operational cost perspectives. In addition to the inherent cost efficiency of EVs, selling energy back to the grid during peak grid demand, is a potential source of additional revenue to a fleet operator. To achieve this, EVs have to be at specific locations (discharge points) during specific points in time (peak period), even while meeting their core purpose of delivering goods to customers. In this work, we consider the problem of EV routing with constraints on loading capacity; time window; vehicle-to-grid energy supply (CEVRPTW-D); which not only satisfy multiple system objectives, but also scale efficiently to large problem sizes involving hundreds of customers and discharge stations. We present QuikRouteFinder that uses reinforcement learning (RL) for EV routing to overcome these challenges. Using Solomon datasets, results from RL are compared against exact formulations based on mixed-integer linear program (MILP) and genetic algorithm (GA) metaheuristics. On an average, the results show that RL is 24 times faster than MILP and GA, while being close in quality (within 20%) to the optimal.
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