The costs for logistics has been increasing over the last few years, especially since the pandemic. This is felt in many supply chains and therefore it is important to explore avenues to reduce these costs. In this research a case study will be performed at MPO. MPO is a supply chain management company thatwould like to improve their services through the use of machine learning. Accordingly, in this research the main research question that will be answered is: "Howcan supply chain management be improved through the use of machine learning?". This study is split into five sub-questions where the first two sub-questions are aimed at the literature of supply chain management and machine learning respectively. The third sub-question dives into the case of MPO and selects an element, which will be looked at further in the research. To answer sub-question four, a conceptual model for the chosen element is made. The fifth and final sub-question measures the impact of conceptual model variants on the results of experiments performed on the selected element...

Currently, literature regarding Multiagent Path Finding (MAPF) does not give a broad enough overview of all the different approaches. Many papers are hard to read and require proper knowledge of MAPF. The goal of this report is to give a global overview of MAPF. To achieve this goal, we provide a detailed explanation of what MAPF problems look like, as well as giving a clear overview of the strength and weaknesses of different solutions. Besides this theoretical analysis, we also analyse and critique benchmarking performed by other researchers. Following all this, we conclude that the field of MAPF lacks agreement on terminology. Furthermore, performance analysis is limited to researchers choice, skewing research in their own favour.

Items being misplaced in warehouses easily get lost. To combat this, warehouses have to send people in scanning all barcodes in the warehouse. This is highly inefficient, which is why Eonics wants to build a drone handling this. There are options out there to scan barcodes, but none of them match the requirements laid out by Eonics. Among these requirements are a lightweight camera, such as a GoPro, and a recording distance of 1.5-2 metres. This report will look and see if these requirements are feasible. Techniques used in this report are Mathematical Morphology, Maximally Stable Extremal Regions, Convolutional Neural Networks, Gradiental Difference and Direction Estimation with Region Extraction. The report concludes in stating that interpreting the barcodes is not possible with mere software under these requirements. The maximal distance we were able to interpret barcodes from, based on a 4K image, was around 1 metre. Continuing the trend, we would need at least an 8K camera to detect from a distance of 1.5 metres. Detection however, is less difficult and is feasible from a distance of 1.5-2 metres. The report also derives an function to use to calculate the maximum distance a barcode can be interpreted from, based on the details of the barcode and camera. Finally, research is done regarding using hardware solutions, such as a zoom-lens, which has promising results.