This paper focuses on the effect of different storage policies on the performance of RoboticMobile Fulfilment Systems (RMFSs). The research is conducted under the instructions of the Technical University of Delft and CEVA Logistics the Hague. The aim of the research is to develop a decision support tool that can aid companies in the choice of the storage policy to use for their RMFS. RMFSs have multiple different levels of decision problems that need to be solved. The performance of a RMFS highly depends on the algorithms that are applied to solve these decision problems. This research focuses only on the storage policies of a RMFS. In this case storage policy refers to the decision in which pod items should be stored and where on the storage area the pod should be positioned. In order to gain a good understanding of the effect of such a policy on the overall performance of a RMFS, experiments should be performed. Physical experiments are however very hard and costly to perform. This research therefore makes use of a simulation study to test different storage policies in different scenarios. The simulation model used is an adaptation on an agent-based semi-open queuing network framework model by Merschformann et al. (2018a). In the experiments four different storage policies are investigated under three different storage layouts. The results of the simulations are analysed via the throughput, the pile-on and the distance travelled during the simulation. After this a score is given to both the picking as well as the replenishment side of the system. It is important to investigate both sides of the system since the flaws on one side can negatively affect the other side. The scores of the replenishment and picking processes are then averaged to gain a final score which indicates the overall performance of the policies. Finally a fifth policy has been developed where each pod can contain multiple different sized compartments. Unfortunately testing and verification of this policy was not possible in the given time frame. For this reason it has been left out of the experiments.

The Automatic Identification System (AIS) is used in the maritime domain to improve sea traffic safety by requiring vessels to broadcast real-time information such as identity, speed, location, and course. As it allows global monitoring of almost any larger vessel and has the potential to considerably improve vessel traffic services and collision risk assessment, AIS has been used in an increasing number of applications. This emphasizes why the quality of data transmitted is critical. We are also becoming more aware of the possibilities of spoofing or fabrication of AIS data, which has a direct impact on the dependability of AIS data. This study looks into comparing video surveillance with Automatic Identification System (AIS) data to detect data overlap or spoofing in the maritime environment. An object detection model was proposed and trained to fill the research gap mentioned above. Data from video surveillance and AIS were collected. A comparison of the results of object detection and AIS data was performed using a Python script. The analysis assisted in identifying data variations and understanding the potential and constraints of the current Algorithm. Work for the future is suggested to tackle the current constraints.

Inland waterway shipping, marked by its unpredictable and variable nature, plays a crucial role in transportation. This research's objective is to address these inconsistencies by constructing a robust scheduling model tailored to waterway systems' specific needs and challenges. The model is enhanced with predictive analytics and optimisation methods to ensure efficient and reliable operations. Within this framework, the research delves deep into four distinct optimisation problems: the Travelling Salesman Problem (TSP), the Job Shop Scheduling Problem (JSSP), the Resource-Constrained Project Scheduling Problem (RCPSP), and the Waterway Ship Scheduled Problem (WSSP). The underlying theme connecting these problems is the application of Graph Neural Networks (GNN) as a tool to model these complex systems. The TSP is a cornerstone in combinatorial optimisation. Specifically, for this research, TSP serves as a means of understanding the challenges and intricacies of inland waterway networks. Employing the GNN model, the research provides insights into potential solutions for TSP within this context. When comparing various TSPLIB instances, the GNN model showcases its efficiency and potential for further refinement, especially in real-world routing and logistics. Shifting the focus towards production planning, the JSSP emerges as a pivotal problem. It aims to optimise the order and timing of tasks for various ships, ensuring minimal usage of time and resources. By implementing the GNN architecture, the research offers a fresh perspective on JSSP. When applied to real-world scenarios, it is evident that the model can predict optimal scheduling sequences, matching the actual time frames and resource allocation required, thereby promising significant advancements in maritime trade efficiency. Diving deeper into operations research, the RCPSP surfaces as a challenge that focuses on optimising project schedules, considering resource constraints and task precedents. The research introduces an approach to address this problem, especially concerning cargo operations within port networks. The research promises efficiency, reliability, and adaptability in Inland Waterway Transport (IWT) scheduling practices by integrating renewable resources and managing precedence relationships. Lastly, the WSSP centres on managing ship movements within a defined time frame, optimising the sequence and timing of vessels to minimise delays and maximise the utilisation of waterway infrastructure and resources. Building upon the foundational work of previous research, this problem was translated and redefined in the context of the Resource-Constrained Project Scheduled Problem. Using this foundation, distinct RCPSP problems were formulated to reflect real-world scenarios, particularly emphasising the port of Duisburg. Drawing upon the results, the GNN model demonstrates high efficiency and accuracy in addressing the WSSP. While traditional tools like OR TOOLS provided optimal results, the GNN model closely mirrored these benchmarks, solidifying its position as a formidable solution for complex scheduling issues, especially given its rapid computation times. In conclusion, this research presents a cohesive understanding of various optimisation problems within the realm of inland waterway shipping, all while harnessing the power of GNNs. Through systematic exploration and application, the research underscores the potential of GNNs to revolutionise how we approach and solve these challenges, promising a future of enhanced efficiency and reliability in waterway shipping operations.