We support decision making using advanced methods. We work cross-disciplinarily and turn complex challenges into opportunities by transforming data into insight.

Today, we have more data available than at any other point in history. In the Decision Science Section, we use data to support decision making through advanced mathematical models of real world systems.

Mathematical tools such as linear and integer programming, heuristics and simulation enable important decisions to be made quantitatively. Models and methods often become complex when designed to reflect the size and structure of real-life problems accurately. Intractable models often are encountered, so development of new solution methods is an important activity.

The Decision Science Section take a holistic approach to operations research by covering all phases of an Operations Research project: from problem analysis to model building and the development of new solutions methods, ultimately to interpret obtained results.

The section is engaged actively in a wide set of application areas such as financial engineering, manpower planning, sustainable transportation planning, health care operations and energy systems planning. In this section, we have established strong research traditions in public transport. Within maritime logistics, the section is recognised as an international leader.

In addition to specific application areas, the section also focuses on generic tools and methods across various application areas, and we utilise tools such as machine learning to strengthen optimization methods that are part of the operations research toolbox.

The section regularly publishes in high-ranking journals such as Computers & Operations Research, Transportation Science, European Journal on Operational Research and Transportation Research, Part B. Several members of the section are associate editors of reputable operations research journals and often serve as members of scientific/organising committees for conferences.