Scarcity of maritime container terminal (MCT) capacity can become a problem for global supply chains. Bottlenecks limit the capacity of these terminals and should therefore be detected and alleviated. However, there is no structured approach available in literature to mitigate the effects of bottlenecks at MCTs. Therefore, this research introduces a holistic approach called the bottleneck mitigation cycle (BMC) which consists of three steps: bottleneck classification, bottleneck detection, and bottleneck alleviation. This research provides a proof of concept of the BMC. Scientific value is added by proposing a contemporary and comprehensive classification structure of bottlenecks at MCTs which consists of infrastructural, operational, and managerial bottlenecks. Infrastructural and operational bottlenecks are the focus of this research. Furthermore, while literature often only focuses on alleviation of a single bottleneck and skips bottleneck detection, this research uses the shifting bottleneck method and thereby considers a variety of possible infrastructural and operational bottlenecks. The shifting bottleneck method originates in production networks and is adapted such that it can be applied to detect both momentary and average bottlenecks at MCTs. An empirical approach is adopted to find the cause of the detected bottleneck and to suggest suitable alleviation measures. Application of the BMC to a simulation model of the Fergusson Container Terminal in the Port of Auckland results in productivity improvements of 2-6%. Due to its generic formulation, the BMC is potentially successful in improving performance of MCTs in general which could be confirmed by future research. To make the BMC even more effective and efficient, future research directions are to improve the empirical approach used for bottleneck alleviation and to apply the BMC in real-time.

Increased international marine transport has increased the use of containers. This goes hand in hand with the increased demand for well-functioning container terminals. The speed at which containers can be handled on the container terminal is an important performance indicator for container terminals. In particular, the productivity of the Quay Crane (QC)s is used to determine the performance of a container terminal. To achieve a high performance, a well designed schedule of operations is required. This research investigates to what extent a coordinated planning can contribute to increasing the QC productivity. Currently, an uncoordinated scheduling heuristic is used to dispatch the equipment operating on a container terminal. With a coordinated schedule, operations of all equipment working on the terminal can be considered at once to achieve optimal QC productivity. A Hybrid Flow Shop (HFS) model with revolutionary features is used to model the waterside of a container terminal in a coordinated way. The revolutionary features are called: bi-directional and job-pairs. The former enables jobs to move through the HFS in both directions, the latter constrains certain jobs to be performed simultaneously by a single machine. Subsequently, a tailored Simulated Annealing (SA) algorithm is used to obtain a heuristic solution to this mathematical model. The performance of the HFS model in combination with the SA algorithm is evaluated with a sophisticated and validated container terminal simulation model developed by TBA. Therefore, the SA algorithm should balance between both quality and computational time. Computational time should not be excessive since the schedule has to be adjusted frequently since operations on a container terminal vary quickly and predictions with regards to processing times are unreliable. To evaluate the performance of the coordinated schedule, it will be compared with the uncoordinated scheduling heuristic of TBA. Based on the results obtained during this research, the QC productivity whilst using TBA's uncoordinated scheduling heuristic cannot be increased with a coordinated schedule that uses a HFS to model the container terminal and a SA algorithm to solve that model (p-value 0.2731). On the other hand, it can neither be concluded that TBA's uncoordinated scheduling heuristic results in higher productivity compared to the coordinated schedule. Therefore, the coordinated schedule is a solid alternative to TBA's uncoordinated scheduling heuristic. However, it is expected that with certain changes to the coordinated schedule the QC productivity could be increased.