HEURISTIC OPTIMIZATION FOR LARGE-SCALE EXAM SESSION PLANNING

Open education systems play a significant role in providing flexible and accessible learning opportunities to large student populations, independent of time and location. These systems achieve cost efficiency through the effective implementation of economies of scale, reducing unit costs as student numbers increase. However, decision-making in the management processes of such systems is crucial. The use of optimization techniques is an essential requirement for enhancing the efficiency and effectiveness of these processes. In particular, applying optimization techniques to the organization of face-to-face centralized exams in these systems can improve overall efficiency. The planning of large-scale face-to-face examinations, especially in education systems with a high number of students or in nationally administered centralized exams, involves significant logistical and scheduling challenges. The planning process for such exams generally begins with the assignment of students to exam sessions. These assignments are directly related to resource utilization within the system and, therefore, significantly impact overall system efficiency. This study proposes a heuristic optimization algorithm to solve the session assignment problem, which involves assigning students to exam sessions under specific constraints. The developed algorithm aims to find a solution based on predefined performance criteria while adhering to the constraints of the examination system. The algorithm was tested using data from Anadolu University’s Open Education System, where it successfully assigned exam sessions for 656 courses taken by nearly one million students while complying with the given constraints. The findings indicate that the algorithm provides feasible solutions under varying performance criteria, effectively minimizing resource utilization.