Online operations strategies for automated multistory parking facilities

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.