Parking at megacities has become a major problem that is garnering increasing attention. The fundamental cause of the parking problem is the imbalance of demand and supply in core areas, where parking demand is high but parking provision is limited owing to exorbitant land prices. The idea of multistory parking facilities is proposed to serve larger parking demands with fewer land possessions. The newly developed automated multistory parking facilities are able to pick-up and place cars on different stories automatically. This paper proposes online operations method (OOM) of automated multistory parking facilities in response to intensive parking demands to reduce customers’ waiting time. The proposed online optimization model is composed of two tiers: in the first tier, a reinforcement learning framework is adopted to determine parking spot selections for incoming parking demands, and the second tier executes the plan acquired from the first tier by optimizing the action sequences of the automated elevator. Numerical experiments with multiple demand patterns are conducted to verify the proposed methodology. The results show that the learned strategy distinguishes from common practice in that it shows non-greedy patterns for some time during the day, and achieves significant improvements in various cases.

在大城市停车已经成为一个日益受到关注的主要问题。停车问题的根本原因是核心地区的供需失衡，这些地区的停车需求很高，但由于土地价格过高，停车供应有限。提出了多层停车设施的想法，以减少土地占用，满足更大的停车需求。新开发的自动多层停车设施能够自动将汽车停放在不同的楼层上。针对大量停车需求，提出了自动多层停车设施的在线操作方法（OOM），以减少客户的等待时间。拟议的在线优化模型由两层组成：在第一层中，采用强化学习框架来确定呼入停车需求的停车位选择，第二层通过优化自动电梯的动作顺序执行从第一层获取的计划。进行了具有多种需求模式的数值实验，以验证所提出的方法。结果表明，所学习的策略与常规做法不同，因为它在一天中的某些时间段显示出非贪婪模式，并且在各种情况下均取得了显着改进。