Predicting and then optimising decisions via deep learning

Project level: Honours, Masters, PhD

Predictive analytics and optimisation are two prominent techniques capable of addressing numerous real-world challenges. The "predict and optimise" paradigm refers to real-world problems where we need to first predict the unknown values of a variable and then optimise some decisions. For instance, one might aim to predict product demand to fine-tune production planning, or forecast electricity demand to optimally schedule machine operations. This approach has manifold applications across sectors like finance, retail, manufacturing, and energy. Within this context, predictions serve as inputs to optimisation models. While heightened prediction accuracy often bolsters optimisation, it doesn't always directly lead to enhanced results. The core challenge we seek to address is the seamless integration of these two phases to craft an end-to-end model that is focused on decision optimisation. Throughout this process, you will hone machine and deep learning models that consider final decisions in their forecasting efforts.