Examination Schedule Management System for Faculty Members at Tay Do University

This paper presents the design and implementation process of an Exam Invigilation Scheduling Management System for lecturers at Tay Do University. The system automates the assignment of invigilators based on real-world data collected during the period 2021–2025. The core solution of the research is the combination of Graph Coloring algorithms to handle time conflicts and Greedy heuristics algorithms to ensure fairness in workload allocation. Exam sessions are modeled as nodes in the graph, where edges represent scheduling conflicts (matching dates and times). The system has been deployed on the Kaggle platform with interactive dashboards, allowing for transparent data analysis and visualization. Experimental results show that the system is capable of: Automatically detecting and eliminating 100% of scheduling conflicts between lecturers and exam rooms. Balancing workloads across departments helps reduce standard deviation in task allocation. Optimizing human resources through faculty rotation using modulo operations. Providing an intuitive query interface by date, class, or faculty member enhances educational management efficiency. This research contributes a data-driven approach, transitioning from manual management processes to intelligent automation systems, suitable for the practical context of Vietnamese universities.