Artificial Intelligence Enhanced Human–Machine Job Scheduling in a Hybrid Printing System: A Review

The review examined the manufacturing context, scheduling challenges, role of artificial intelligence (AI), review methodology, key findings, and research implications. Hybrid printing integrates traditional, digital, and additive manufacturing technologies to enhance flexibility, customization, material efficiency, and production capacity. However, traditional rule-based and static optimization approaches are unable to effectively handle the complex scheduling challenges created by the coexistence of heterogeneous machines, diverse materials, dynamic production requirements, maintenance constraints, and human involvement. Thus, the review looks at how artificial intelligence (AI) methods such as machine learning, deep learning, reinforcement learning, multi-agent systems, swarm intelligence, and explainable AI (XAI) are applied to intelligent scheduling in hybrid printing environments. In the context of the Industry 5.0 paradigm, it further explores human-in-the-loop methods that integrate computational optimization with human knowledge, contextual judgment, and operational oversight. In order to assess scheduling objectives, algorithms, performance metrics, technological architectures, implementation difficulties, and new research directions, the review synthesizes recent academic literature. Results show that makespan, resource utilization, throughput, adaptability, energy efficiency, and responsiveness to production disruptions can all be improved with AI-enhanced scheduling. Real-time decision-making, transparency, and operator trust are further improved by integration with digital twins, XAI, and the Industrial Internet of Things (IIoT). The review comes to the conclusion that collaborative scheduling between humans and AI offers a promising basis for creating intelligent, robust, and sustainable hybrid printing systems.