University of Nottingham

Network Rail Fully Funded PhD Studentship – An optimisation approach for railway network recovery actions in response to disruptions

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Reference: ENG1586

Closing Date: Saturday, 31st December 2022

Department: Civil Engineering

Research Group:  Resilience Engineering 

Applications are invited for this 3.5 year PhD project, from suitably qualified graduates to work in the Resilience Engineering Research Group, based in the Faculty of Engineering, University of Nottingham, University Park. The University of Nottingham has worked with Network Rail, as its Strategic University Partner in Infrastructure Asset Management, for over 10 years and our Research Group specialises in the development of models to support the asset management process.  

Background:

The UK’s Rail Technical Strategy (RTS) 2020 sets a 20-year vision for railway service quality with a focus on being safe, reliable and resilient, as well as meeting capacity and service requirements, through innovation and technology. This is a major challenge, when in addition to component and system failures, and human errors, modern railways are now also subject to a range of additional threats, such as cyber-attacks, natural hazards, and climate change. All such disruptions create a major challenge for delivering safe, reliable and resilient service to passengers where a rapid recovery becomes of great importance in service delivery and passenger experience. 

The Project:

The current practice to choose a recovery action after a disruption is based on pre-written contingency plans and, to some degree, relies on controller judgement without the ability to explore a variety of options automatically. Such complex decisions need to consider several different factors, such as the location of the disruption on the network, the current timetable, the level of perturbation in the network, etc. To ensure an effective and quick recovery plan, the controller needs to make a decision about which trains should be cancelled, rerouted, terminated short of the planned terminus, or regulated in a different way to minimise the disruption. The best decision is the one which delivers the least impact on train performance, and this can be measured in different ways, e.g. the amount of reactionary delay, cancellations or on time failures, or the count of trains or passengers affected. There is a clear need to be able to explore this large variety of options and factors in an automated way for a better-informed decision-making process to recover after a disruption. 

The project will focus on developing a methodology to investigate the best recovery actions in response to a disruption. The methodology will explore a range of decision options based on the properties of the disruption and priority criteria of the controller. The ambition of the project is how to evaluate a range of best strategies and support the decision-making process. 

Potential candidates for the proposed methodology include the methods of discrete event simulation, network vulnerability analysis and multi-objective optimisation, supported by data mining techniques, such as machine learning and neural network approaches. 

The funding available will cover UK PhD tuition fees plus a tax-free stipend for three and a half years, starting at £15,999 pa.  International students are welcome to apply with their own funding. The successful candidate will have (or will be about to receive) at least an upper second class degree in mathematics, engineering, physics or computer science with strong skills in modelling.

For further information, or to discuss this opportunity in more detail, please contact Dr Rasa Remenyte- Prescott r.remenyte-prescott@nottingham.ac.uk

This post will remain open until filled

Informatie over de vacature

Functienaam
Network Rail Fully Funded PhD Studentship – An optimisation approach for railway network recovery actions in response to disruptions
Locatie
University Park Nottingham, Verenigd Koninkrijk
Gepubliceerd
2022-08-22
Uiterste sollicitatiedatum
2022-12-31
Soort functie
PhD
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