Optimisation and supervised learning for decision making: competitors or partners?
Abstract
Machine learning (ML) is a constantly growing research area. While optimisation researchers are trying to understand how to take advantage of ML methods, ML researchers are developing algorithms to solve classical optimisation problems. Given the huge impact of ML in a large variety of disciplines and application domains, an underlying discussion topic concerns the possibility that ML may become capable of replacing optimisation. As ML is a very broad research area, we focus on supervised learning, one of the most prominent approaches. In this paper we discuss the different roles of optimisation and supervised learning in decision making processes, provide a high level overview of the ways in which each can enrich and benefit the other, and highlight research directions for an impactful interaction of the two paradigms.
Fields of Study & Subjects
Complex Systems and Decision Making
Organizational Learning and Leadership
Operations Management Techniques
Publication details
- Journal / source
- Journal of the Operational Research Society
- Publisher
- Informa UK Limited
- Volume
- 77
- Issue
- 6
- Pages
- 1643–1652
- Publication date
- 21 January 2026
- Language
- EN