Optimal design and planning for compact automated parking systems
Compact automated parking (CAP) systems are fully automated parking systems, which store cars densely. Such systems are mainly used in congested cities all over the world, providing rapid parking access and safe vehicle storage. We study a prominent new technology, with relatively low cost and rapid response. The system has a rotating ring equipped with shuttles in each tier for horizontal transport, and uses two lifts in the middle of the CAP system for vertical transport. We present a dedicated lift operating policy under which it uses one lift for storage and another for retrieval, and a general operating policy under which it uses both lifts for storage and retrieval. We propose queuing networks for single-tier and multi-tier systems based on two different policies for operating the lifts (a dedicated and general operating policy). We validate the analytical models using simulation based on a real application. We also conduct a sensitivity analysis in which we vary speeds of lifts and car rotation. Then we use the analytical models to optimize the system layout by minimizing the retrieval time. Furthermore, combining time efficiency and system cost, we find an appropriate system layout for designers. Third, we compare two lifts under dedicated and general operating policies. Forth, we find the optimal number of the lifts through a general compact automated parking system. Finally, we calculate the investment cost of a CAP system under different system configurations and compare it with an alternative design: a cubic parking system.
|Keywords||Compact automated parking system, Logistics, Queuing network and simulation modeling, System performance, Warehousing|
|Persistent URL||dx.doi.org/10.1016/j.ejor.2018.09.014, hdl.handle.net/1765/110936|
|Journal||European Journal of Operational Research|
Wu, G. (Guangmei), Xu, X, Gong, Y, de Koster, M.B.M, & Zou, B. (2018). Optimal design and planning for compact automated parking systems. European Journal of Operational Research. doi:10.1016/j.ejor.2018.09.014