Confident data-driven Decision Support

2026-07-22

The CoRDS project addresses building the next generation of artificial intelligence (AI)-powered decision support tools to allow organizations to tackle complex decision-making problems more effectively and responsibly,  such as efficiently managing scarce (natural) resources and reducing their carbon footprints. These tools unify two areas of research, namely Operations Research (OR) and Machine Learning (ML). In OR, specialized optimization methods have been developed to address complex decision problems, but these rely heavily on expert knowledge, limiting their ability to adapt to changing data. Conversely, ML excels in leveraging extensive data for predictive tasks, but struggles with combinatorial optimization. Integrating OR and ML, leading to data-driven optimization (DDO) tools, presents a promising avenue to enhance decision support by combining OR's problem-solving capabilities with ML's data utilization strengths. Furthermore, DDO tools must not only provide high-quality decisions to users in low computational time, they must also comply with government and industry standards, and therefore must be safe, transparent, traceable and non-discriminatory, i.e., follow the principles of trustworthy AI, a significant challenge for most current AI systems. The expertise needed to create and apply DDO methods to real-world problems is severely lacking. The CoRDS doctoral network addresses this critical need by developing a training program to sculpt the next generation of analytics experts combining OR and ML, who will translate their research into prototype tools to address real-life problems defined in collaboration with our industrial partners across various application sectors, including logistics, healthcare, public transportation, production, finance, publishing and machine translation. The CoRDS network further delivers a training framework for others to use and expand.

Austria, Denmark, Latvia, Netherlands, Ireland, Spain, Italy, United Kingdom, Belgium, Germany

2026-02-01