Optimizing Learning Factory Operations Through Just-in-Time Scheduling Methodologies

The rapid evolution of manufacturing systems requires the development of advanced scheduling methodologies to enhance operational efficiency and productivity. This paper explores the application of Just-in-Time (JIT) scheduling in the context of learning factories that simulate real-world production environments for educational purposes. By integrating lean manufacturing principles with state-of-the-art optimization algorithms, our aim is to address the dynamic and complex nature of modern production settings. Our research highlights the transition from traditional scheduling methods to algorithmdriven approaches, and also giving a small highlights about the last works with industry 4.0. This study identifies key gaps in current practices and proposes innovative solutions to optimize production schedules, ultimately contributing to the advancement of learning factory operations.