<![CDATA[Course timetabling in higher education is a complex combinatorial problem due to constraints related to lecturer availability, limited classroom resources, and fixed weekly time-slot structures. As the number of courses and class sections increases, manual scheduling becomes increasingly inefficient and prone to conflicts, particularly room clashes and overlapping lecturer assignments. This study develops and evaluates an automatic course scheduling system based on the Ant Colony Optimization (ACO) algorithm and implements it as a Java-based desktop application to generate feasible timetables under real institutional conditions. An experimental computational approach is employed, in which artificial ants construct candidate schedules through probabilistic selection influenced by pheromone trails and heuristic information. Timetable quality is evaluated using a weighted cost function that prioritizes hard-constraint satisfaction, such as preventing lecturer and room clashes, while also incorporating soft-constraint penalties related to lecturer forbidden timeslots and schedule distribution balance. The system is tested using real academic data from an undergraduate study program, including courses, lecturers, classrooms, and predefined weekly timeslots. Experimental results show that the proposed system consistently generates conflict-free timetables, achieving a conflict value of zero across all repeated runs under the selected parameter configuration. Beyond feasibility, the optimization process continues to refine timetable quality by reducing soft-constraint penalties, as indicated by the convergence behavior observed across repeated executions. This repeated-run evaluation provides insight into the stochastic optimization characteristics of the ACO-based approach under fixed parameter settings. These findings indicate that the Java-based ACO approach effectively supports automated university course scheduling and provides a practical solution for producing feasible and well-structured timetables.]]>
