A Hybrid Genetic Algorithm–Tabu Search Approach for AI-Driven Exam Timetabling in Higher Education

Abstract

The increasing scale and complexity of higher education institutions have rendered manual and rule-based examination scheduling methods inadequate for managing resource constraints, student diversity, and institutional policies. This paper presents a hybrid Artificial Intelligence (AI)–driven examination timetabling system that integrates Genetic Algorithms (GA) and Tabu Search (TS) to generate conflict-free, efficient, and equitable timetables. The GA component performs global exploration to identify optimal scheduling permutations, while TS executes local refinement to prevent convergence toward suboptimal solutions. Developed using Django (backend), React (frontend), and MySQL (database), the system automates examination scheduling, optimizes venue and invigilator allocation, and adapts dynamically to academic changes. Empirical evaluation using real datasets from Mulungushi University demonstrated a 97.5% reduction in exam conflicts, substantial improvement in scheduling efficiency, and enhanced satisfaction among students and administrators. The proposed hybrid GA–TS framework provides a scalable and modular foundation for future AI-based scheduling research, contributing to the advancement of intelligent academic management systems in higher education.