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.

Citation for the May Hicks Awards 2022

The OR Society is delighted to announce the winners of the 2022 May Hicks prize for Best Post-graduate Project. The winner, Laura Hannula (University of Southampton), received £1000. The runners up were Nikolas Heinloth (University of Edinburgh) and Hassan Bukhari (Loughborough University), each of whom received £250 each.

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


Laura Hannula (University of Southampton)

Project ‘Exploring the business value of free text data and the use of problem structuring in the Wärtsilä Marine Power Sales organisation’

Wärtsilä is a global leader in innovative technologies and lifecycle solutions for the marine and energy markets, operating in more than 70 countries. While numerical data is widely and successfully used across the company to support customers, Laura’s dissertation focused on generating actionable insights from less commonly utilised free-text data. 

The topic for the dissertation stemmed from the need to better collect and use the innovative ideas and opinions of customer-facing staff in management decision-making without engaging in resource-intensive qualitative interviews. Wärtsilä’s Marine Power Sales team trialled data collection via an ad-hoc staff survey, which became the source data for the exploratory dissertation project in the summer of 2021.  

Laura’s dissertation focused on assessing the benefits and challenges associated with using free-text data. Her practical work highlighted the immense value of drawing insights from free-text comments written by employees of various levels of seniority and using these to establish a development roadmap. 

Her dissertation is an excellent demonstration of theoretical knowledge obtained from MSc Operational Research studies applied to a real-life business problem to create value and actionable insights.


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Nikolas Heinloth - May Hicks Runner-up.jpg

Nikolas Heinloth (University of Edinburgh)
Project ‘Islay Hydrogen Project’

Nikolas's project set out to assess the economic feasibility and required system design of a transition to a hydrogen-based energy system on the island of Islay, located off the west coast of Scotland. Islay is home to an over-proportionately high number of whisky distilleries, so fuel oil and kerosene currently dominate Islay's energy demand (80%), rendering the island a key target for industrial decarbonisation. Moreover, the island is adjacent to the offshore wind farm development area W1, scheduled to go into operation by the early 2030s. It could provide the electric power needed to fully support sufficient green hydrogen production to meet the potential hydrogen demand on the island, enabling a green energy transition.

Hassan Bukhari May Hicks Runner -up 2022.jpg

Hassan Bukhari (Loughborough University)
Project ‘Prioritising Zoonotic Diseases in Rio Negro, Argentina using Multi-Criteria Decision Evaluation'

Emerging zoonotic diseases are a cause for global concern and pose a significant threat. These diseases are a growing international problem that continuously transcends the human-animal-environment interface. Hassan's project was proposed by the University of Surrey in collaboration with Loughborough University and carried out with the help of the National University of Rio Negro. The project was to develop a decision model that helps prioritise five prevalent diseases in the Argentinian province of Rio Negro region after a thorough and systematic multi-criteria evaluation. he study had a formidable impact as it was one of the first evaluations to provide a systematic MCDA model to prioritise zoonoses in Rio Negro, Argentina. 

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