The intersection of machine learning with forecasting and optimisation: theory and applications
This work is incremental, proposing integration without new methods or results.
The paper tackles the insufficient exploration of integrating forecasting and optimization to handle uncertainties, advocating for their intersection with machine learning to address real-world problems and providing research directions.
Forecasting and optimisation are two major fields of operations research that are widely used in practice. These methods have contributed to each other growth in several ways. However, the nature of the relationship between these two fields and integrating them have not been explored or understood enough. We advocate the integration of these two fields and explore several problems that require both forecasting and optimisation to deal with the uncertainties. We further investigate some of the methodologies that lie at the intersection of machine learning with prediction and optimisation to address real-world problems. Finally, we provide several research directions for those interested to work in this domain.