Design and Implementation on Intelligent Scheduling System for Computer Laboratory Management

In the context of the growing prevalence of educational automation, laboratory teaching management is facing increasingly complex challenges driven by the demands of new technologies. However, traditional manual scheduling methods and existing scheduling systems have revealed several limitations in practical application, including their inability to accommodate adjustments and changes in teaching plans, lack of transparency in scheduling information, and inefficiencies in resource sharing. To address these issues, we have developed a Java-Web-based intelligent scheduling system. This system not only offers a more user-friendly interface and robust data analytics capabilities, but also employs reinforcement learning algorithms to quantitatively evaluate scheduling effectiveness, thereby optimizing laboratory resource utilization and course arrangements. The system can automate most routine scheduling tasks, significantly alleviating the workload of administrators. Compared to existing automated laboratory scheduling software, our system demonstrates significant advantages in both scheduling accuracy and efficiency. This positions it as a more intelligent, precise, and efficient solution for higher education management.