Designing an Optimal Scheduling Framework for Balancing Lecturer Workload and Student Timetables in Large Academic Institutions: A Case Study of Ankara Medipol University, Türkiye

Abstract

Academic scheduling in large universities has long suffered from inefficiencies that compromise faculty well-being, student engagement, and optimal use of institutional resources. Traditional and semi-manual scheduling systems often result in unequal teaching loads, scattered student timetables, and underutilized classrooms—issues that persist even in technologically advanced institutions. This paper addresses these challenges by proposing a Mixed Integer Programming (MIP)-based optimization framework designed specifically for large academic environments like Ankara Medipol University in Türkiye that was used as the case study of this research. The model integrates faculty availability, course enrollment, departmental room assignments, and student learning patterns to generate balanced, conflict-free timetables. Using 30 real-world scheduling instances across five faculties, the model was implemented and validated with institutional data and stakeholder feedback. Findings revealed a 58.3% improvement in workload equity, a 51% reduction in student idle time, and a 29.3% boost in classroom utilization efficiency. Beyond these metrics, the model significantly improved academic satisfaction, reduced operational workload for administrators, and provided university leadership with a data-driven tool for strategic scheduling. This research not only offers a scalable and adaptable scheduling solution but also sets a precedent for integrating mathematical modeling with institutional governance and educational equity. Universities seeking to modernize their planning systems and improve academic quality can adopt this framework as a practical, evidence-informed solution.