A Genetic Algorithm Approach for University Course Timetabling: A Case Study at the Faculty of IT, Sebha University
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Abstract
University course timetabling is a complex optimization problem, crucial for the efficient operation of educational institutions. Manual methods are often time-consuming, error-prone, and struggle to handle the increasing complexity of constraints and resources, leading to conflicts and inefficiencies. This paper presents the development and implementation of an automated timetabling system for the Faculty of Information Technology at Sebha University, Libya, utilizing a Genetic Algorithm (GA). The system aims to generate feasible and optimized class schedules by considering various hard and soft constraints, including lecturer availability, room capacity, course requirements, and avoiding clashes for students and lecturers within the same timeslot. The system was developed using Python with the Django framework and SQLite database. Experiments were conducted using real data from the faculty. The results demonstrate that the GA-based system successfully generated conflict-free timetables (zero hard constraint violations) within a reasonable computation time (approx. 193-257 seconds) and number of generations (approx. 101-126). This automated approach provides a significant improvement over the faculty's previous manual process, which often required several days of work and still resulted in persistent scheduling conflicts. This work significantly improves upon manual methods and addresses the specific challenges faced by the faculty.
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