Stochastic modeling of unloading and loading operations at a container terminal using automated lifting vehicles
With the growing worldwide trade, container terminals have grown in number and size. To increase operational efficiency, many new terminals are now automated. The key focus is on improving seaside processes, where a distinction can be made between single quay crane operations (all quay cranes are either loading or unloading containers) and overlapping quay crane operations (some quay cranes are loading while others are unloading containers). Using a network of open and semi-open queues, we develop a new integrated stochastic model for analyzing the performance of overlapping loading and unloading operations that capture the complex stochastic interactions among quayside, vehicle, and stackside processes. The analytical model is solved using an iterative algorithm based on the parametric decomposition approximation approach. The system performance is tested at varying container traffic levels. We find that the percent absolute errors in throughput times compared to simulation are less than 10% for all cases. Using these integrated models, we are able to generate design insights and also rapidly analyze what-if scenarios. For example, we show that the best yard layout configurations for single (either loading or unloading) operations and the best for overlapping (both loading and unloading) operations largely overlap. The best configurations have relatively few stack blocks and many rows per block. The model is generic and amenable to obtain other design and operational performance insights.
|Keywords||Logistics, Queueing theory, Stochastic modeling, Transportation|
|Persistent URL||dx.doi.org/10.1016/j.ejor.2017.10.031, hdl.handle.net/1765/103072|
|Journal||European Journal of Operational Research|
Roy, D, & de Koster, M.B.M. (2017). Stochastic modeling of unloading and loading operations at a container terminal using automated lifting vehicles. European Journal of Operational Research. doi:10.1016/j.ejor.2017.10.031