A Metaheuristic Hybrid Approach for University Timetabling- Genetic Algorithm and Simulated Annealing

Abstract

This study addresses the recurrent course scheduling problem in universities. The problem involves constructing an optimal timetable by allocating courses, lecturers, and student groups to rooms and time slots while satisfying mandatory hard constraints and improving quality through soft constraints. Given the scale—nine study programs, 148 courses, 123 classrooms, 82 class groups, and 147 active lecturers—the problem exhibits combinatorial complexity. We propose a hybrid metaheuristic that integrates Genetic Algorithm (GA) and Simulated Annealing (SA) to balance global exploration and local exploitation. GA is selected for its robust exploration of large solution spaces and its proven applicability to university timetabling, while SA offers principled local refinement guided by an annealing schedule to reduce constraint violations. Prior work indicates that GA–SA hybrids can improve convergence and reduce computation time relative to standalone GA. We formalize the constraints, define a fitness function that prioritizes feasibility, and design neighbourhood operators tailored to timetabling moves. The proposed approach aims to deliver a robust timetable that satisfies institutional requirements and enhances operational efficiency.