The intersection of machine learning with forecasting and optimisation: theory and applications

TL;DR

This paper explores the integration of machine learning with forecasting and optimisation, highlighting their interrelationship, methodologies, and potential for solving real-world problems involving uncertainty.

Contribution

It advocates for combining forecasting and optimisation through machine learning and discusses methodologies and future research directions in this interdisciplinary area.

Findings

Identifies the relationship between forecasting, optimisation, and machine learning.

Proposes methodologies at the intersection for real-world applications.

Suggests future research directions in integrated forecasting and optimisation.

Abstract

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.