New container terminals are embracing robotized transport vehicles such as lift-automated guided vehicles (LAGVs) and automated guided vehicles (AGVs) to enhance the terminal throughput capacity. Although LAGVs have a high container handling time, they require less coordination with other terminal equipment in comparison with AGVs. In contrast, AGVs are hard-coupled resources, require less container handling times, but operate with high coordination delays in comparison with LAGVs. The effect of such operational trade-offs on terminal performance under various design parameter settings, such as yard block layout and a number of resources, is not well understood and needs to be evaluated at the terminal design phase. To analyze these trade-offs, we develop stylized semi-open queuing network models, which consist of two-phase servers and finite capacity queues. We develop a novel network decomposition method for solving the proposed queuing models. The accuracy of the solution method is validated using detailed simulation models. Using the analytical models, we study the performance trade-offs between the transport vehicle choices: LAGVs and AGVs. Our results show that the throughput capacity of the terminal in the container unloading process increases by up to 16% if LAGVs are chosen as transport vehicles instead of AGVs. However, at certain parameter settings, specifically, when the arrival rate of containers is low, the throughput time performance of the terminal is higher (up to 8%) with AGVs than with LAGVs. We also derive insights on the yard block layout and the technology choice for quay cranes.

Additional Metadata
Keywords blocking, container terminal, Logistics, semi-open queues, transport vehicles, two-phase servers
Persistent URL dx.doi.org/10.1080/24725854.2020.1785648, hdl.handle.net/1765/129650
Journal IISE Transactions
Citation
Kumawat, G.L. (Govind Lal), & Roy, D. (2020). AGV or Lift-AGV? Performance trade-offs and design insights for container terminals with robotized transport vehicle technology. IISE Transactions. doi:10.1080/24725854.2020.1785648