Thanks to digitalisation, the available amount of data to support decision-making has reached a high level of maturity, and so has the capability of processing this data. Alongside the data science disciplines, optimisation — used for decision support — plays a key role in the further development of tools to support advanced decision-making. The scale and complexity of the problems relevant to address continue to increase and this calls for research that address the mathematics and algorithms needed in optimisation methods.
We believe that truly intelligent decision-making is achieved through an integration of model-based and data-driven approaches that are designed or used in interaction with a human decision-maker. To successfully apply mathematics and algorithms as part of real-world decision-making requires careful mathematical modelling and data collection. An inherent property of many decision problems of practical relevance is that they are computationally challenging. Solving such a problem within a reasonable amount of time often requires the development of specialised methods that exploit the mathematical structure of the problem.