Distributed dynamics scheduling based reinforcement learning: importance and challenges

When it comes to scheduling choices inside complex industrial systems, the dynamic job shop scheduling problem (DJSSP) poses substantial difficulties. Deep learning, artificial intelligence (AI), and reinforcement learning approaches have all shown promising solutions in recent years to enhance the effectiveness and performance of DJSSP systems. This study provides a detailed analysis of the DJSSP literature, with an emphasis on these cutting-edge methods. The review adopts a rational methodology that includes an exhaustive search of many databases from 1995 to 2023. Articles were chosen based on predetermined qualifying criteria, taking into account their applicability to the DJSSP and the methodology used. This paper also examines how software design affects research, how industry influences research, and the types of research outputs that are disclosed. The findings provide valuable insights into the current state of research and offer guidance for future advancements in optimizing dynamic job-shop scheduling using advanced learning techniques