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META-HEURISTICS  May 2012

META-HEURISTICS May 2012

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Subject:

PhD (EngD) project in "Heuristics for Optimisation of Large-Scale Maintenance Scheduling using Big Data" at the University of York, UK

From:

Peter Cowling <[log in to unmask]>

Reply-To:

Peter Cowling <[log in to unmask]>

Date:

Fri, 4 May 2012 12:51:33 +0100

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EngD in Artificial Intelligence (AI)/Operational Research (OR) - fully funded with fees plus a 4-year tax-free stipend of £16,705 p.a.

Project Title: Extending Asset Life through Optimised Maintenance using Grid-Based Decision Support

University of York - Department of Computer Science

Applications are invited for a student to work on a research project in computerised decision support systems which can use geolocated condition data to generate plans and schedules which greatly improve the efficiency of asset maintenance, and hence prolong the life of assets. The annual bill for this maintenance is billions of pounds per year in the UK alone, and this project has the potential for significant environmental and financial impact in this area of long-term practical and research interest, as well as considering some challenging tasks in modelling and heuristics for real-world combinatorial optimisation. The project is supervised and sponsored by Gaist Ltd, and will require research and experimentation in modelling, decision support, heuristic optimisation, software engineering and databases.

The project is fully funded and will be carried out in conjunction with studying for an Engineering Doctorate in Large Scale Complex IT Systems at the University of York. The EngD is a full time, 4-year doctoral level research degree involving both taught and research components.

The Sponsoring Organisation: Gaist Ltd:
Gaist is an award-winning SME with bases in Lancashire and Yorkshire, specialising in the capture and fusion of data related to condition monitoring and maintenance. Gaist are developing advanced data fusion, decision support and visualisation methods in a parallel research project, which will bring together data from a wide range of systems into a single, large database of unprecedented detail and accuracy. Gaist work closely with UK urban borough councils and the Department for Transport, to enhance decision making within the highways infrastructure domain, with the ultimate aim being to promote innovative methodology and to realise significant efficiency savings at national and international levels.

The Research Project:
This project will investigate how advanced modelling and Artificial Intelligence (AI)/Operational Research (OR) techniques can aid in the creation of plans and schedules to enhance the planning of life-prolonging asset maintenance. By using Gaist's Grid-based data approach, up-to-the-minute condition data can be used to allocate maintenance resources precisely where they are most needed, rather than basing maintenance schedules on past practice or historical data. Our decision support systems will also take into account social factors, such as the need for access to facilities such as hospitals along specific roads.
  Initially we will consider maintenance for roads and footways and associated street furniture such as lampposts, crossings, road markings etc., although our approaches may be extended, by subsequent projects, to other geographically-dispersed maintenance activities such as building
maintenance, maintenance of railway tracks and tramways, hedge and grass cutting, agriculture, etc.

Full project details and further particulars can be found at www.cs.york.ac.uk/engd/.

The Academic Supervisor for this EngD will be Professor Peter Cowling (www.scim.brad.ac.uk/~picowlin), who has an international reputation for work in heuristic optimisation and AI. He will take up a Chair at the University of York this summer.

Entry Requirements, Funding and Applications:
Applicants should be highly motivated and have a minimum of an upper second-class honours degree in Computer Science or a related discipline (e.g. Maths with Computing, Electrical Engineering). Significant programming experience, practical knowledge of AI/OR techniques, and modelling expertise are essential. Knowledge of decision support and geospatial information technologies is helpful. Applicants with specialist knowledge or experience in artificial intelligence or operational research are particularly welcome to apply.

The successful applicant will receive fees and a tax-free stipend from the Engineering and Physical Sciences Council (EPSRC) of £16,705 p.a.  Please note there are eligibility requirements - see http://www.epsrc.ac.uk/funding/students/pages/eligibility.aspx

For further information on the EngD and how to apply for this position please visit www.cs.york.ac.uk/engd/

Informal enquiries can be made to Mrs Dawn Forrester [email: [log in to unmask] ]

Closing date for applications is 11th May 2012.

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