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The ever-increasing worldwide container throughput and the intensified port competition have demanded better terminal operational performance, which is usually measured by the gross crane rate (GCR). In this paper, a multiagent-based simulation model is proposed to investigate how the block length of the storage yard and the tactical-level stacking policy affect the operational performance of non-transshipment container terminals over the long term. Experiments consider seven block lengths, two yard truck fleet sizes and two stacking policies. The results demonstrate that the best block length yielding the highest GCR is dependent on the stacking policy and the yard truck fleet size, and the separate stacking policy is essentially superior to the scattered stacking policy. Specifically, only when 9-yard trucks are deployed per quay crane under the separate stacking policy, can the typical 42-slot block length achieve the highest GCR. Although the experiments are not exhaustive, they do provide the first fundamental insights, with respect to the effects of block lengths and stacking policies, on the long-term performance at a manually controlled non-transshipment container terminal. [ABSTRACT FROM AUTHOR]

For the security of ports and the container shipping industry, a key problem for a container terminal is the planning and design of the inspection area. The main goal of this paper is to address an inspection investment planning problem for the international container terminal at Dalian Port using the simulation method. Considering various types of container ships and flexible container truck scheduling and routing, we propose a framework combining an Arena-based simulation model with a developed optimization procedure. Two components of this integrated framework realize information communication via the interface of Visual Basic for Applications. Under the potential inspection protocol, simulation experiments are designed and carried out for investigating the operational impact of container inspections for different allocations of inspection resources at the terminal, including the turnaround time of external trucks, the number of trucks waiting in the inspection area, etc. This paper includes the description of the conceptual framework, the detailed simulation model, the experimental design, and the simulation results. [ABSTRACT FROM AUTHOR]