发表机构
Tsinghua University; Columbia University（清华大学; 哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电动汽车电池更换站的非平稳排队系统，提出连续时间MDP方法，推导出显式阈值结构的最优控制策略，并通过数值实验验证其鲁棒性。
AI 中文摘要
我们研究了一个具有多个并行服务器和有限缓冲区容量的非平稳排队系统，时间范围为有限规划期。受电动汽车（EV）电池更换站核心操作的启发——即如何最佳管理从电动汽车上换下的电池的充电问题，我们开发了一种连续时间马尔可夫决策过程（MDP）方法来解决该问题。主要的技术挑战在于允许到达率和服务成本率都是时间的函数，且在离散时间点存在跳跃，这是电动汽车更换站运行的关键特征。我们推导出了一个精确且显式的阈值结构，该结构刻画了最优控制策略。此外，我们给出了阈值随时间单调的条件，以及相对于关键系统参数（如服务和缓冲区容量）单调的条件。我们进行了大量的数值实验，以补充和验证理论结果，并说明阈值结构在实际实现中的鲁棒性。
英文摘要
We study a non-stationary queueing system with multiple parallel servers and a finite buffer capacity, over a finite planning horizon. Motivated by an operation that is central to an EV (electric vehicle) battery-swapping station -- how to best manage the charging of batteries swapped off from EVs, we develop a continuous-time MDP (Markov decision process) approach to the problem. The main technical challenge is to allow both the arrival rate and the service-cost rate to be functions of time with jumps at discrete time points, key features of the EV swapping station's operation. We derive an exact and explicit threshold structure that characterizes the optimal control policy. Moreover, we give conditions under which the thresholds are monotone over time, and with respect to key system parameters such as service and buffer capacities. Extensive numerical experiments are carried out to supplement as well as validate the theoretical results, and also to illustrate the robustness of the threshold structure in practical implementations.
