Timetable scheduling is a complex and resource-intensive task, particularly in academic institutions offering multiple courses with diverse classroom and faculty requirements. Traditional manual methods are time-consuming, error-prone, and often fail to optimize resource utilization. This study proposes an intelligent timetable scheduling system using machine learning techniques to automate and optimize multi-course classroom allocation. The proposed model considers constraints such as faculty availability, classroom capacity, course overlap, and student enrollment patterns. By applying supervised and reinforcement learning approaches, the system dynamically generates conflict-free schedules while ensuring optimal resource distribution. Experimental results demonstrate improved efficiency, reduced scheduling conflicts, and enhanced adaptability compared to traditional methods. This intelligent scheduling framework provides a scalable and robust solution that can be customized for different institutional requirements.
Intelligent scheduling, machine learning, timetable optimization, classroom allocation, reinforcement learning, supervised learning, resource utilization, conflict-free scheduling, higher education, automation.