论文标题
基于节点的基于图形的在线汽车重新平衡政策，并通过电容电荷充电
A node-charge graph-based online carshare rebalancing policy with capacitated electric charging
论文作者
论文摘要
电动汽车共享操作的生存能力取决于重新平衡算法。文献中的较早方法表明，使用排队原理的非侧重算法的趋势。我们使用成本函数近似提出了新的重新平衡策略。成本函数被建模为P-Median搬迁问题，在静态节点充电图结构上具有最低成本流量和基于路径的充电站能力。成本函数是NP完整的，因此提出了一种启发式，以确保可以在在线系统中解决的可行解决方案。该算法在纽约布鲁克林的电动汽车案例研究中得到了验证，该算法在2017年9月共享了BMW Reachnow操作中的需求数据（262个车队，每天231个接送机，每天231个接送服务，303个交通分析区（TAZS）（TAZS）（TAZS））和收费站位置数据（有4个带有4个港口功能的充电站）。拟议中的非侧重平衡启发式启发式使近视的重新平衡降低了38％。进一步讨论了其他管理见解。
Viability of electric car-sharing operations depends on rebalancing algorithms. Earlier methods in the literature suggest a trend toward non-myopic algorithms using queueing principles. We propose a new rebalancing policy using cost function approximation. The cost function is modeled as a p-median relocation problem with minimum cost flow conservation and path-based charging station capacities on a static node-charge graph structure. The cost function is NP-complete, so a heuristic is proposed that ensures feasible solutions that can be solved in an online system. The algorithm is validated in a case study of electric carshare in Brooklyn, New York, with demand data shared from BMW ReachNow operations in September 2017 (262 vehicle fleet, 231 pickups per day, 303 traffic analysis zones (TAZs)) and charging station location data (18 charging stations with 4 port capacities). The proposed non-myopic rebalancing heuristic reduces the cost increase compared to myopic rebalancing by 38%. Other managerial insights are further discussed.
