Decision Science

We improve decision-making to create value in settings that impact millions of people daily, such as public transport networks, healthcare operations, and energy systems. We work cross-disciplinarily and turn complex societal challenges into actionable solutions by transforming data into insight.

We draw on disciplines such as applied mathematics, statistics, and computer science to develop both application-specific and general methodological frameworks. Our research spans mathematical programming, exact and heuristic optimisation methods, simulation, and machine learning to enable quantitative decision-making in complex real-world systems.

The Decision Science Section takes a scientific approach to decision-making by covering all phases of principled problem solving, from problem analysis to data gathering, model building, and the development of new solution methods, ultimately to extract managerial insights from experimental results. Through rigorous modelling and algorithm design, our research provides evidence-based approaches that make well-founded and transparent decision-making feasible at the scale and complexity of real-world systems.

The impact of our work is amplified by our active engagement in a wide set of application areas such as urban mobility, transportation, workforce planning, healthcare operations, and green transition challenges in the maritime sector, energy systems, and process industries. We are proud of having established a strong research tradition in public transport and of being recognised as an international leader within maritime logistics.