Machine learning algorithms in scheduling problems: an overview and future paths

Abstract

Scheduling is an important process that can have a significant impact on the productivity of a company. This literature review explores how Machine Learning algorithms have been used to solve scheduling problems. This article is composed of several stages: the two most significant areas – Scheduling and Machine Learning - are examined, a bibliometric analysis of the existing literature is performed, and case studies in the areas of Scheduling and Machine Learning are analyzed. The bibliometric analysis evidenced the recent growth of this research area. Several supervised learning algorithms are used to solve scheduling problems, although the reinforcement learning ones have seen considerable advances in recent years. They are applied to autonomously solve real-world problems and for enhancing the performance of traditional optimization techniques, such as Metaheuristics. Improving characteristics of these techniques, such as their exploration capabilities, has shown significant developments, however, it is still limited to a certain number of Metaheuristics. Therefore, in future research, it would be interesting to use these algorithms to enhance the performance of less-explored Metaheuristics and thereby overcome their main challenges. Machine Learning stands out as an emerging field with the potential to positively contribute to the development of effective strategies capable of solving scheduling problems.