论文标题
部分可观测时空混沌系统的无模型预测
A Reinforcement Learning Approach for Electric Vehicle Routing Problem with Vehicle-to-Grid Supply
论文作者
论文摘要
从可持续性和运营成本的角度来看，在最后一英里中使用电动汽车（EV）引起了人们的吸引力。除了电动汽车的固有成本效率外，在高峰电网需求期间将能源销售回电网是舰队运营商额外收入的潜在来源。为了实现这一目标，即使在特定的时间点（高峰期），即使达到向客户交付货物的核心目的，EV也必须在特定位置（排放点）。在这项工作中，我们考虑了对加载能力限制的电动汽车路由问题。时间窗口；车辆到网格的能源供应（CEVRPTW-D）；这不仅满足了多个系统目标，而且还有效地扩展到涉及数百个客户和出院站的大型问题。我们提出了使用加固学习（RL）进行电动汽车路由来克服这些挑战的Quikroutefinder。使用所罗门数据集，将RL的结果与基于混合构成线性程序（MILP）和遗传算法（GA）元硫素化的精确配方进行了比较。结果平均显示，RL比MILP和GA快24倍，而质量接近最佳（20％以内）。
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.
