Airport operations are undergoing significant change, having to meet pandemic requirements in addition to intrinsic security requirements. Although air traffic has declined massively, airports are still the critical hubs of the air transport network. The new restrictions due to the COVID-19 pandemic pose new challenges for airport operators in redesigning airport terminals and managing passenger flows. To evaluate the impact of COVID-19 restrictions, we implement a reference airport environment. In this Airport in the Lab environment we will demonstrate the operational consequences derived from the new operational requirements. In addition, countermeasures to mitigate any negative impacts of these changes are tested. The results highlight emerging issues that the airport will most likely face and possible solutions. Finally, we could apply the findings and lessons learned from our testing at our reference airport to a real airport.
The constant growth of air traffic, especially in Europe, is putting pressure on airports, which, in turn, are suffering congestion problems. The airspace surrounding airport, terminal manoeuvring area (TMA), is particularly congested, since it accommodates all the converging traffic to and from airports. Besides airspace, airport ground capacity is also facing congestion problems, as the inefficiencies coming from airspace operations are transferred to airport ground and vice versa. The main consequences of congestion at airport airspace and ground, is given by the amount of delay generated, which is, in turn, transferred to other airports within the network. Congestion problems affect also the workload of air traffic controllers that need to handle this big amount of traffic.This thesis deals with the optimization of the integrated airport operations, considering the airport from a holistic point of view, by including operations such as airspace and ground together. Unlike other studies in this field of research, this thesis contributes by supporting the decisions of air traffic controllers regarding aircraft sequencing and by mitigating congestion on the airport ground area. The airport ground operations and airspace operations can be tackled with two different levels of abstractions, macroscopic or microscopic, based on the time-frame for decision-making purposes. In this thesis, the airport operations are modeled at a macroscopic level.The problem is formulated as an optimization model by identifying an objective function that considers the amount of conflicts in the airspace and capacity overload on the airport ground; constraints given by regulations on separation minima between consecutive aircraft in the airspace and on the runway; decision variables related to aircraft entry time and entry speed in the airspace, landing runway and departing runway choice and pushback time. The optimization model is solved by implementing a sliding window approach and an adapted version of the metaheuristic simulated annealing. Uncertainty is included in the operations by developing a simulation model and by including stochastic variables that represent the most significant sources of uncertainty when considering operations at a macroscopic level, such as deviation from the entry time in the airspace, deviation in the average taxi time and deviation in the pushback time. In this thesis, optimization and simulation techniques are combined together by developing two methods that aim at improving the solution robustness and feasibility. The first method acts as a validation tool for the optimized solution, and it improves the robustness of solution by iteratively fine-tuning some of the optimization model input parameters. The second method embeds the optimization in a simulation environment by taking full advantage of the sliding window approach and creating a loop for a continuous improvement of the optimized solution at each window of the sliding window approach. Both methods prove to be effective by improving the performance, lowering the total amount of conflicts up to 23.33% for the first method and up to 11.2% for the second method, however, in contrast to the deterministic method, the two methods they are not able to achieve a conflict-free scenario due to the effect of uncertainty.In general, the research conducted in this thesis highlights that uncertainty is a factor that affects to a large extent the feasibility of optimized solution when applied to real-world instances, and it, moreover, confirms that using simulation together with optimization has the potentiality toivdeal with uncertainty. The framework developed can be potentially applied to similar problems and different optimization solving methods can be adapted to it.Keywords: Optimization, Simulation, Integrated airport operations, Uncertainty
MULTIFILE
Airport capacity, expressed as the maximum number of air traffic movements that can be accommodated during a given period of time under given conditions, has become a hard constraint to the air transportation, due to the scarce amount of resources on the ground and restrictions in the airspace. Usually the problem of capacity at airports is studied separating airspace operations from ground operations, but it is evident that the two areas are tied to each other. This work aims at developing a simulation model that takes into account both airspace and ground operations. The approach used is a divide and conquer approach, which allows the combination of four different models. The four models refer to the airside, and airspace operations. This approach allows to evaluate the system from diffrent angles depending on the scope of the study, the results show the analytic potential of this approach.
Organs-on-chips (OoCs) worden steeds belangrijker voor geneesmiddelonderzoek. Het kweken van miniatuurorganen in microfluïdische chips creëert een systeem waarmee geneesmiddelonderzoekers efficiënt geneesmiddelen kunnen testen. OoCs kunnen in de toekomst een belangrijk instrument voor personalized medicine worden: door het kweken van patiëntmateriaal in OoCs kan dan worden bepaald welke interventies voor specifieke patiënten werken en veilig zijn. In de huidige praktijk worden cellulaire veranderingen in OoCs na blootstelling aan een geneesmiddel doorgaans gevolgd met visualisatietechnieken, waarmee alleen effecten van geneesmiddelen kunnen worden waargenomen. Voor bepaling van de voor geneesmiddelonderzoek cruciale parameters absorptie, distributie, metabolisme en excretie (ADME) is het noodzakelijk om de concentraties van geneesmiddelen en hun relevante metabolieten te meten. Het doel van AC/OC is dit mogelijk te maken door het ontwikkelen van analytisch-chemische technieken, gebaseerd op vloeistofchromatografie gekoppeld met massaspectrometrie (LC-MS). Hiermee kunnen ontwikkelaars van OoCs (de eindgebruikers van AC/OC) de voordelen van hun producten voor geneesmiddelonderzoek beter onderbouwen. Dit project bouwt voort op twee KIEM-projecten, waarin enkele veelbelovende analytisch-chemische technieken succesvol zijn verkend. In AC/OC zullen wij: 1. analytisch-chemische methodes ontwikkelen die geschikt zijn om een breed scala aan geneesmiddelen en metabolieten te bepalen in meerdere types OoCs; 2. deze methodes verbeteren, zodat de analyse geautomatiseerd, sneller en gevoeliger wordt; 3. de potentie van deze methodes voor geneesmiddelonderzoek met OoCs demonsteren door ze toe te passen op enkele praktijkvraagstukken. Het OoC-veld ontwikkelt zich razendsnel en Nederland (georganiseerd binnen OoC-consortium hDMT) speelt daarin een belangrijke rol. AC/OC verbindt kennis en expertise op het gebied van analytische chemie, OoCs, celkweek en geneesmiddelonderzoek. Hierdoor kan AC/OC een bijdrage leveren aan sneller en betrouwbaarder geneesmiddelonderzoek. Met de ontwikkeling van een minor ‘OoC-Technology’, waarin we de onderzoeksresultaten vertalen naar onderwijs, spelen we in op de behoefte aan professionals met kennis, ervaring en belangstelling op het gebied van OoCs.
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
