Automated Class Scheduling System (ACSS) for ISUFST

Manual class scheduling in higher education institutions is a complex and time-consuming administrative task that often results in conflicts, overlapping instructor assignments, room double-bookings, and inefficient allocation of classroom resources. Such inefficiencies can lead to operational delays, reduced teaching quality, and increased administrative workload. This study presents the development and implementation of an Automated Class Scheduling System (ACSS) specifically designed for ISUFST to address these challenges. The ACSS leverages a constraint-based scheduling algorithm to automatically assign instructors, classrooms, and time slots while ensuring adherence to institutional rules and minimizing conflicts. The system considers various constraints, including instructor availability, room capacity, appointment types (lectures and laboratories), course-year-section consistency, and batch allocations, to generate optimized timetables.The system is implemented as a desktop application using Windows Presentation Foundation (WPF) and follows the Model–View–ViewModel (MVVM) architecture, with Microsoft SQL Server as the backend database for data management and persistence. Administrative functionalities include managing instructors and subjects, defining room capacities and configurations, setting availability schedules, and exporting generated schedules to image or PDF formats. Evaluation of the ACSS demonstrated its effectiveness in reducing manual scheduling time, preventing conflicts, and improving overall administrative efficiency. The automated generation of color-coded timetables by instructor, room, and section facilitates clear visualization and flexibility in scheduling adjustments.By integrating intelligent constraint handling and user-configurable parameters, the ACSS provides a practical, reliable, and scalable solution suitable for the academic environment of ISUFST. Future enhancements may include the incorporation of student enrollment data and the adoption of advanced optimization techniques, such as genetic algorithms, to support larger institutions and further improve schedule efficiency.