Balancing the trade-off between increasing demand of airport capacity and tighter environmental regulations has been recognised by the European Commission as one of the grand challenges. To better utilise the existing airport infrastructure and reduce pollution, future aerodrome traffic management systems, incorporating Trajectory Based Taxi Operations (TBTO), are of great importance. Aligned with the European ASMGCS and US NextGen programmes, future aerodrome operations based on TBTO will coordinate aircraft movements (manned/unmanned) to specific locations on the airport surface at specific times, leading to time, fuel and cost savings and increased safety. Regarding TBTO the decision on which aircraft to solve first and where to hold the aircraft and for how long affects possible solutions of the remaining aircraft and availability of scarce resources such as runways and gates. Therefore, it is paramount and timely to further develop such a framework to incorporate constraints from other airside operations, enable intermediate holding, and understand interdependencies between aircraft. Due to many possible interactions between aircraft, it is not straightforward to predict the consequences of complex and interdependent decisions. Common approaches by both humans and machines break the challenge into small parts, leading to suboptimal solutions.
This is an EPSRC Industrial CASE Studentship sponsored by EPSRC and the UK's leading air traffic control services provider – NATS. The aims of this PhD include:
(1) Discovering, analysing, and using the knowledge of interdependencies to develop a decomposition algorithm for efficiently solving large and complex optimisation problems. This insight of interdependence could help not only human operators but also optimisation algorithms in efficiently decomposing large problems into smaller ones which are faster and easier to solve.
(2) Designing search algorithms working on individual subcomponents in a cooperative way towards a global optimal solution. The developed search algorithms will take into account the constraints at runways and gates, and determine the optimal holding times at the origin and intermediate positions of taxiing.
The project will develop the fundamental science in operational research and artificial intelligence technologies to deliver an entirely new category of Air Traffic Management (ATM) decision support tools for future airport airside operations, contributing towards the development of airport airside digital twins. The student will work closely with NATS, and the team specialised in ATM at Queen Mary University of London (QMUL), and benefit from having both academic and industry supervisors from QMUL and NATS.
Courses will be offered in many different subject areas via the host university. This includes research relevant courses, career and professional management, publishing, research design, writing skills through the existing CDT in Data centric Engineering. The student will be supported by the industry lead and team. There will be access to training courses at NATS and access to the NATS training college, its staff and curriculum and other materials. There will also be access to R&D resources including the NATS collaborative cloud computing platform including storage and compute and access to proprietary software toolboxes and libraries.
EPSRC Industrial CASE Studentship - This UKRI studentship is fully funded and includes a 4 years stipend (set at £19,668 for 2022/23) and fees at the home level.
Apply at https://www.sems.qmul.ac.uk/research/studentships/525
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