Project level: Honours, Masters, PhD

Predictive and prescriptive analytics are two widely used techniques that together can solve many real-world problems. The paradigm of "predict and optimise" is useful when we are dealing with dynamic decision-making problems. For example, you may be interested to predict the demand for products and accordingly optimise your production planning, or you may be interested in predicting the electricity demand and optimise your scheduling for machines. There are many other examples in finance, retail, manufacturing, and energy domain where this paradigm would be useful. In this class of problems, the prediction will be used as n input to the optimisation model and often a better accuracy leads to better optimisation. However, the production accuracy will not directly translate to improvement in optimisation. The problem that we would like to answer is how we can integrate these two phases and develop an end-to-end model that can optimise the decisions. You will develop machine learning and deep learning models that take into account the final decisions in forecasting.

Project members

Dr Mahdi Abolghasemi