| dc.description.abstract | Academic timetabling remains a critical and inherently complex task within higher
education institutions, formally classified as an NP-hard optimization problem. It
involves allocating courses, resource persons, classrooms, and student groups under
numerous hard constraints such as resource persons availability and classroom capacity,
and soft constraints such as minimizing gaps between student lectures or optimizing
room usage. Traditional manual or rule-based scheduling methods often struggle
with these complexities, resulting in conflicts, underutilized resources, and increased
administrative workload. In response, Artificial Intelligence and metaheuristic-based
approaches including Simulated Annealing, Genetic Algorithms, Tabu Search, and
hybrid frameworks have emerged as effective solutions for automated, conflict-free
timetable generation. This review critically examines five influential studies that
exemplify methodological innovations in hyper-heuristics, genetic algorithms, and
hybrid metaheuristics, highlighting the transition from static heuristics to adaptive
learning-based models. The study identifies key challenges in scalability, interpretability,
real-time adaptability, and integration with institutional systems. The findings suggest
that hybrid AI frameworks and adaptive heuristic selection mechanisms are among
the most promising approaches, enabling efficient schedule generation and supporting
informed administrative decision-making. | en_US |
