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 [email protected] to arrive no later than 30 April.

Citation for the May Hicks Awards 2024

The OR Society is delighted to announce the winners of the 2024 May Hicks prize for Best Post-graduate Project.

The winner, Scott Jenkins (University of Edinburgh), received £1000. The runners up were Saumya Singh (University of Southampton) and Tom Murarik (University of Edinburgh) who received £250 each.

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.

Winner:

Scott Jenkins (University of Edinburgh)

Project ‘Optimal Strategy for Grid-Scale Batteries Participating in Power Markets and Frequency Response Services’

This project focused on developing a data-driven approach to optimise the bidding strategies for grid-scale batteries participating in both wholesale power markets and frequency response services.

Scott developed explainable probabilistic methods to address the core challenge of market uncertainty. These methods provided clear and interpretable results, which allow Flexitricity to not only optimize battery revenue but also effectively communicate strategies to their internal and external customers. Scott's work had three main elements:

  • A novel simulation methodology for generating probabilistic auction outcomes.
  • A mixed-integer linear optimisation model to determine the optimal strategy for battery utilisation across the day, considering both frequency response auctions and wholesale power markets.
  • A pricing methodology was developed to identify the bid prices that maximise expected revenue for Flexitricity in the frequency response auctions. 

As renewable resources are brought on to the network, National Grid ESO increasingly look to batteries to balance supply and demand to maintain system stability. By maximising the return on investment for its customers, Flexitricity encourages more storage systems to be developed, allowing more renewable energy to be utilised efficiently. 

Scott's work aligns perfectly with Flexitricity's vision to enable Great Britain’s transition to Net Zero through market-leading innovation, leadership, and delivery of flexibility in the energy sector. Scott continues his work in this space as a Data Scientist in Flexitricity’s Data team and continues to adapt his findings and approach to the current market opportunities and design.

Runners-up

Saumya Singh (University of Southampton)

Project ‘Development and Implementation of a Calendar Scheduling Algorithm ’

Saumya’s dissertation concerned improving the scheduling of meetings at The Ford Motor Company, which is completed by hand, relying heavily on trial-and-error and human judgement. With increasing numbers of employees and meetings, this task becomes increasingly challenging even before considering the different locations and time zones where employees are located.
Saumya presents a greedy penalty-based heuristic in her dissertation that efficiently balances speed, ease of use, and interpretability. Saumya uses randomisation to increase the effectiveness of the heuristic. The resulting method is not only cost-effective, but it also allows for user-friendly visualisation.
The dissertation reports promising results and confirms the potential of automated scheduling tools compared to manual approaches. The results of the project offer a valuable tool for businesses and organisations seeking to optimise their meeting schedules while considering diverse preferences and operational constraints. This development is poised to streamline scheduling processes, bolster productivity and employee satisfaction, and ultimately contribute to the success of various ventures.

Tom Murarik (University of Edinburgh)
Project ‘Feature Selection for Customer Churn Classification Using Multi-Objective Evolutionary Algorithms’

Tom’s dissertation concerned the use of a Feature Selection Genetic Algorithm (FSGA) to address the telecommunications industry problem of customer churn at Vodafone, whilst providing versatility across various Machine Learning problems and datasets. This involved developing a novel Multi-Objective Evolutionary Algorithm called the FSGA. The FSGA employs Multi-Objective Optimization to enhance decision-making, consistently improving model performance by up to 20% and validating feature importance. It combines ten feature selection methods and a mix of genetic algorithm techniques and specialised local search operators.
Tom evaluated the methodology on diverse datasets, including real-world commercial ones. The FSGA's journey from dissertation to near-publishable state and integration into live commercial software environments highlights its transformative impact and widespread adoption potential.
Tom now works for Optrak, where his dissertation work has continued to be a source of utility.