May Hicks Award

The OR Society funds its annual awards for student projects from a generous bequest from the estate of Mrs May Hicks, wife of Donald Hicks OBE, a major contributor to operational research and the first treasurer of The OR Society. Projects entered are OR projects carried out for a client organisation rather than within the university.


Entries for any year should be submitted electronically to to arrive no later than 30 April.

Citation for the May Hicks Awards 2023

The OR Society is delighted to announce the winners of the 2023 May Hicks prize for Best Post-graduate Project. The winner, Matt Tench (University of Edinburgh), received £1000. The runner up was Aviel Jailal (University of Warwick) who received £500.

Thanks go to the Universities who submitted their student projects. It was pleasing to read about such a range of business problems and how well they are being addressed by OR techniques.


Matt Tench (University of Edinburgh)

Project ‘Measuring Airport Operator Accountable Resilience’

This project focused on fundamentally understanding what it means for an airport to be resilient and was commissioned by London Gatwick, the 8th busiest airport in Europe and 2nd busiest in the UK.

The overall goal was to inform stakeholders about resilience, making it a topic of objective discussion rather than one of opinion and subjectivity. A mutual understanding across all stakeholders of what ‘resilience’ meant and who needed to improve what was key. Matt’s dissertation aimed to answer two crucial questions for the aviation industry:

  1. What constitutes an airport operator’s accountability for resilience?
  2. How can resilience be accurately measured in this complex ecosystem involving various organisations?

Matt developed the world’s first method to isolate the specific resilience that an airport operator should be accountable for. This method involved analysing historical flight data on a case-by-case basis and applying formal tests to classify each movement as either ‘resilient’ or not. By doing so, relevant subsets of operators requiring additional scrutiny were identified, leading to proposed targeted changes and investment for enhancing resilience.

The project built upon fundamental principles of operational management, grounded in queuing theory, to devise effective resilience tests. Two innovative charts, the ‘resilience pipe’ and ‘resilience square,’ were introduced to present the results and insights from the test in a clear and understandable manner.

By answering these critical questions and providing a practical approach to assessing and improving operational resilience, Matt’s project made a significant impact on London Gatwick’s understanding and ability to address resilience challenges. Furthermore, as what happens at one airport will impact every other airport that has connecting flights to it, Matt and the team have begun sharing the ideas with key stakeholders with the ultimate aim of improving resilience across the worldwide aviation network.




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Aviel Jailal (University of Warwick)
Project ‘Detecting SIMBox Fraud Using Machine Learning ’

Aviel’s dissertation considers the application of Machine Learning (ML) methods to automatically detect SIMBox fraud which accounts for an estimated $US3.1 billion (8%) out of a total of $US40 billion lost worldwide to telecommunications fraud (in 2021). More specifically, SIMBox fraud accounts for a cost of $US1.5 - $US2.1 million annually for the Caribbean based client organisation that this work was carried out for. A collaboration with a Mobile Network Operator (MNO) was necessary to advance existing ML research in this domain as it allowed Aviel to validate different ML methods on confidential (anonymized) call detail records provided by the client organisation. Following an informative literature review, Aviel used state-of-the-art statistical methods and visualisations to perform an exploratory data analysis and fraud subscriber profiling analysis, revealing previously unknown insights about fraudster behaviour relating to call volumes, cell sites, and credits. These insights/correlations alone have the potential to signal early warnings about potential frauds, and can be of practical use to other MNOs.