A literature review on deep reinforcement learning for machine scheduling problems

Abstract

Machine scheduling represents a core challenge in industrial production systems due to its inherently complex combinatorial nature and its critical role in enhancing operational efficiency. With recent advances in artificial intelligence, deep reinforcement learning (DRL) has gained increasing attention as an innovative tool to address scheduling tasks with a self-adapting, data-driven approach. This survey presents a comprehensive review of 143 publications between 2018 and February 2025 that apply DRL to machine scheduling problems. We develop a structured framework to classify and compare problem settings, algorithmic designs, and evaluation methodologies. Key aspects such as action and observation space design, reward functions, neural network architectures, and experimental benchmarks are systematically analyzed. The review identifies current trends, outlines promising patterns, and highlights open research opportunities for DRL-based scheduling solutions. The goal of this survey is to make the rapidly evolving research landscape more accessible to both academics and practitioners and to identify the next steps in research and application. To facilitate reproducible research and customized analysis, we publish the dataset underpinning this review, which includes 61 annotated features per publication, allowing for customizable filtering and further in-depth exploration of niches within the field. This dataset is publicly accessible online.