A Review of Deep Reinforcement Learning Methods for Production Scheduling

With the rapid digital transformation of manufacturing industries in recent years, effective production scheduling has become imperative for maintaining competitiveness and addressing operational challenges. Modern manufacturing involves a complex interplay of elements, such as equipment, raw materials, and operators, making effective scheduling crucial for timely and specification-compliant production. However, achieving accurate and efficient scheduling remains a formidable challenge. Deep Reinforcement Learning (DRL), which integrates Deep Learning (DL) and Reinforcement Learning (RL), has emerged as a powerful tool for tackling these complex scheduling problems. A considerable number of related researches have been published in the past five years, yet there exists no review literature for them. Therefore, this paper reviews 167 studies published between Jan. 2019 and July 2024 that utilized DRL methods to solve production scheduling problems. We classify these studies based on types of scheduling problems they address and summarize their approaches to state representation, action space, reward mechanisms, and training algorithms. Additionally, we explore the integration of the DRL methods with optimization algorithms. Finally, we analyze the current research trends and future directions for the application of DRL in production scheduling from both problem-centric and method-centric perspectives.