# Resource Capacity Requirement for Multi-Terminal Cooperation in Container Ports
## 5 Conclusions  
When a container terminal collaborates with its neighboring terminals within a port or across ports in the form of resource sharing, the operations in the collaborating terminals inevitably become more complicated. To realize the gains from such collaborations, an operations management system that can make use of the available resources efficiently and effectively needs to be in place. This research studies three of the most important port resources, namely, QCs, yard, and gate, to manage the capacity requirements over terminals. A resource profile simulation, which provides a platform for simulating random components of resource profiles and estimating the workload on the resources over time-shifts, is developed. The estimated workloads decide capacity requirement on the resources as represented by the QC intensity, the storage space requirement, and gate congestion. The experiment results provide \(\overline{X}-R\) charts for QC intensity, storage space requirement, and gate congestion. The significance tests are examined for the rates of trans-shipment containers compared to inbound and outbound containers, and the rates of transferring containers to other terminals. For different settings of trans-shipment and transferring rates, there was a statistically significant effect of the trans-shipment and transferring rates on the capacity requirement representing workload of the QCs, yard, and gate. The two-factor ANOVA results supported the statistical significance of the trans-shipment and transferring rates, as well as their interaction terms on capacity requirements. In particular, high trans-shipment rates are found to contribute to the reduced workloads for QCs, yard, and gate in terms of capacity requirement when the transferring rates take positive effect on distributing workloads among the terminals for the trans-shipment rates. The realism of the discussed multiterminal capacity requirement planning could be increased with a study of fidelity that estimates the capacity requirements at the equipment-level (e.g., yard cranes, vehicles, etc.) and operational-level (e.g., single vs. dual cycling) while considering the financial commitment (e.g., operational and capital costs for resources). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Higher (75\%): Transferring**} & \multicolumn{3}{c}{**Lower (60\%): Transferring**} \\ \hline  & & **Higher (85\%)** & **Norm (80\%)** & **Lower (75\%)** & **Higher (85\%)** & **Norm (80\%)** & **Lower (75\%)** \\ \hline  & Upper (90\%) & \(d=040\) & \(d=2.43\) & \(d=0.03\) & \(d=0.22\) & \(d=0.52\) & \(d=0.65\) \\  & Upper (90\%) & \(r=0.10\) & \(r=0.52\) & \(r=0.01\) & \(r=0.05\) & \(r=0.13\) & \(r=0.16\) \\ \cline{2-8} QC intensity & Higher (85\%) & - & \(d=2.83\) & \(d=0.42\) & - & \(d=0.31\) & \(d=0.87\) \\  & & \(r=0.58\) & \(r=0.11\) & - & \(r=0.08\) & \(r=0.21\) \\ \cline{2-8}  & Norm (80\%) & - & - & \(d=2.40\) & - & - & \(d=1.18\) \\  & Upper (90\%) & \(d=7.17\) & \(d=28.22\) & \(d=52.88\) & \(d=24.33\) & \(d=45.41\) & \(d=58.66\) \\  & & \(r=0.87\) & \(r=0.99\) & \(r=1.00\) & \(r=0.99\) & \(r=1.00\) & \(r=1.00\) \\ \cline{2-8} Storage space requirement & Higher (85\%) & & \(d=21.05\) & \(d=45.70\) & - & \(d=21.08\) & \(d=34.33\) \\  & & \(r=0.98\) & \(r=1.00\) & - & \(r=0.98\) & \(r=0.99\) \\ \cline{2-8}  & Norm (80\%) & - & - & \(d=24.66\) & - & - & \(d=13.24\) \\  & Upper (90\%) & \(d=0.56\) & \(d=2.46\) & \(d=2.36\) & \(d=0.15\) & \(d=0.61\) & \(d=0.54\) \\  & & \(r=0.14\) & \(r=0.52\) & \(r=0.51\) & \(r=0.04\) & \(r=0.15\) & \(r=0.13\) \\ \cline{2-8} Gate congestion & Higher (85\%) & - & \(d=1.90\) & \(d=1.81\) & - & \(d=0.79\) & \(d=0.39\) \\  & & & \(r=0.43\) & \(r=0.41\) & - & \(r=0.19\) & \(r=0.10\) \\ \cline{2-8}  & Norm (80\%) & - & - & \(d=0.09\) & - & - & \(d=1.15\) \\  & Norm (80\%) & - & - & \(r=0.02\) & - & - & \(r=0.28\) \\ \hline \hline \end{tabular}
\end{table}