Airports represent the major bottleneck in the air traffic management system with increasing traffic density. Enhanced levels of automation and coordination of surface operations are imperative to reduce congestion and to improve efficiency. This paper addresses the problem of comparing different control strategies on the airport surface to investigate their impacts and benefits. We propose an optimization approach to solve in a unified manner the coordinated surface operations problem on network models of an actual hub airport. Controlled pushback time, taxi reroutes and controlled holding time (waiting time at runway threshold for departures and time spent in runway crossing queues for arrivals) are considered as decisions to optimize the ground movement problem. Three major aspects are discussed:1) benefits of incorporating taxi reroutes on the airport performance metrics; 2) priority of arrivals and departures in runway crossings; 3) tradeoffs between controlled pushback and controlled holding time for departures. A preliminary study case is conducted in a model based on operations of Paris Charles De-Gaulle airport under the most frequently used configuration. Airport is modeled using a node-link network structure. Alternate taxi routes are constructed based on surface surveillance records with respect to current procedural factors. A representative peak-hour traffic scenario is generated using historical data. The effectiveness of the proposed optimization methods is investigated.
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This study focuses on the feasibility of electric aircraft operations between the Caribbean islands of Aruba, Bonaire, and Curaçao. It explores the technical characteristics of two different future electric aircraft types (i.e., Alice and ES-19) and compares their operational requirements with those of three conventional types currently in operation in the region. Flight operations are investigated from the standpoint of battery performance, capacity, and consumption, while their operational viability is verified. In addition, the CO2 emissions of electric operations are calculated based on the present energy mix, revealing moderate improvements. The payload and capacity are also studied, revealing a feasible transition to the new types. The impact of the local climate is discussed for several critical components, while the required legislation for safe operations is explored. Moreover, the maintenance requirements and costs of electric aircraft are explored per component, while charging infrastructure in the hub airport of Aruba is proposed and discussed. Overall, this study offers a thorough overview of the opportunities and challenges that electric aircraft operations can offer within the context of this specific islandic topology.
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In this paper, a general approach for modeling airport operations is presented. Airport operations have been extensively studied in the last decades ranging from airspace, airside and landside operations. Due to the nature of the system, simulation techniques have emerged as a powerful approach for dealing with the variability of these operations. However, in most of the studies, the different elements are studied in an individual fashion. The aim of this paper, is to overcome this limitation by presenting a methodological approach where airport operations are modeled together, such as airspace and airside. The contribution of this approach is that the resolution level for the different elements is similar therefore the interface issues between them is minimized. The framework can be used by practitioners for simulating complex systems like airspace-airside operations or multi-airport systems. The framework is illustrated by presenting a case study analyzed by the authors.
ATAL: Automated Transport and Logistics Automatisering van transportmodaliteiten is overal ter wereld gaande. Met een Duurzaam Living Lab kunnen multimodale geautomatiseerde transportoperaties verder in de praktijk duurzaam en opschaalbaar worden ontwikkeld. Hierbij worden beleidsmakers en organisaties ondersteund in deze transitie. De maatschappelijke voordelen van grootschalige uitrol van Automated Trucks en Platooning, Automated Train Operations en Autonomous Sailing zijn onder andere minder energieverbruik en emissies, betere doorstroming en betere verkeersveiligheid. De Duurzame Living Lab heeft betrekking op het haven-achterland vervoer van Rotterdam richting Duitsland en België. Het wegvervoer maakt gebruik van de TULIP-Corridor, water en spoor modaliteit volgen de MIRT goederencorridors tot in het Ruhrgebied.
Client: Foundation Innovation Alliance (SIA - Stichting Innovatie Alliantie) with funding from the ministry of Education, Culture and Science (OCW) Funder: RAAK (Regional Attention and Action for Knowledge circulation) The RAAK scheme is managed by the Foundation Innovation Alliance (SIA - Stichting Innovatie Alliantie) with funding from the ministry of Education, Culture and Science (OCW). Early 2013 the Centre for Sustainable Tourism and Transport started work on the RAAK-MKB project ‘Carbon management for tour operators’ (CARMATOP). Besides NHTV, eleven Dutch SME tour operators, ANVR, HZ University of Applied Sciences, Climate Neutral Group and ECEAT initially joined this 2-year project. The consortium was later extended with IT-partner iBuildings and five more tour operators. The project goal of CARMATOP was to develop and test new knowledge about the measurement of tour package carbon footprints and translate this into a simple application which allows tour operators to integrate carbon management into their daily operations. By doing this Dutch tour operators are international frontrunners.Why address the carbon footprint of tour packages?Global tourism contribution to man-made CO2 emissions is around 5%, and all scenarios point towards rapid growth of tourism emissions, whereas a reverse development is required in order to prevent climate change exceeding ‘acceptable’ boundaries. Tour packages have a high long-haul and aviation content, and the increase of this type of travel is a major factor in tourism emission growth. Dutch tour operators recognise their responsibility, and feel the need to engage in carbon management.What is Carbon management?Carbon management is the strategic management of emissions in one’s business. This is becoming more important for businesses, also in tourism, because of several economical, societal and political developments. For tour operators some of the most important factors asking for action are increasing energy costs, international aviation policy, pressure from society to become greener, increasing demand for green trips, and the wish to obtain a green image and become a frontrunner among consumers and colleagues in doing so.NetworkProject management was in the hands of the Centre for Sustainable Tourism and Transport (CSTT) of NHTV Breda University of Applied Sciences. CSTT has 10 years’ experience in measuring tourism emissions and developing strategies to mitigate emissions, and enjoys an international reputation in this field. The ICT Associate Professorship of HZ University of Applied Sciences has longstanding expertise in linking varying databases of different organisations. Its key role in CARMATOP was to create the semantic wiki for the carbon calculator, which links touroperator input with all necessary databases on carbon emissions. Web developer ibuildings created the Graphical User Interface; the front end of the semantic wiki. ANVR, the Dutch Association of Travel Agents and Tour operators, represents 180 tour operators and 1500 retail agencies in the Netherlands, and requires all its members to meet a minimum of sustainable practices through a number of criteria. ANVR’s role was in dissemination, networking and ensuring CARMATOP products will last. Climate Neutral Group’s experience with sustainable entrepreneurship and knowledge about carbon footprint (mitigation), and ECEAT’s broad sustainable tourism network, provided further essential inputs for CARMATOP. Finally, most of the eleven tour operators are sustainable tourism frontrunners in the Netherlands, and are the driving forces behind this project.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
