Effect of Service Priority and Resource Synchronization Choices on Landside Terminal Queues: Exact Analysis and Approximations

Abstract

With the growth of ocean transport and with increasing vessel sizes, managing congestion at the landside of container terminals has become a major challenge. A terminal landside handles containers that arrive or depart via train or truck. Large terminals have to handle thousands of trucks and dozens of trains per day. As trains run on fixed schedule, their containers are prioritized in stacking and internal transport handling. This has consequences for the service of other modes, which might be subject to delays. We analyze the dynamic interactions between the landside resources using a stochastic stylized semi-open queuing network model with bulk arrivals, shared resources, and multi-class containers. We use the theory of regenerative processes and Markov chain analysis to analyze the network. The proposed network solution algorithm works for large-scale systems and yields sufficiently accurate estimates for performance measurement. The model can capture priority service for containers at shared resources (such as stack cranes), while preserving strict handling priorities. The model is used to explore the choice of different internal transport vehicles (coupled versus decoupled operations at stack and train gantry cranes) to understand the effect on delays. Our results show that decoupled transport resources can mitigate both the delays of containers that arrive by trucks and by trains. When train arrival rates are low, prioritizing the handling of train containers at the stack cranes significantly reduces their delays. Further, this priority has little effect on the delays of handling external truck containers.

A survey on computational intelligence approaches for intelligent marine terminal operations

“…The major problems in the landside operations include: straddle carriers (SCs) operation optimization [36], truck appointment optimization to avoid truck congestion and reduce truck turnaround time [37], double cycling (i.e. loading and unloading simultaneously to enhance productivity) scheduling [38], route and schedule planning of rail‐mounted gantry cranes for efficient exchange of containers between the terminal and road [39], scheduling appointments of container trucks [40], and internal transport vehicle selection to avoid delays and enhance terminal performance [41].…”

Loading and unloading trains at the landside of container terminals

“…Stochastic models are becoming increasingly popular because they can efficiently take the complexity of container terminal operations into consideration. Recently, using a semi-open queuing network (SOQN), Roy and de Koster (2018), Roy et al (2020Roy et al ( , 2016, ), and Dhingra et al (2017 have developed separate integrated queuing models to analyze the design choices in seaside operations. The models are useful for analyzing the impact of vehicle dwell point, effect of stack configuration, and vehicle guide path design on throughput times.…”