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Tan, J.S., Goh, S.L., Kendall, G. and Sabar, N.R. (2021) A Survey of the State-Of-The-Art of Optimisation Methodologies in School Timetabling Problems. Expert Systems with Applications, 165, Article ID: 113943.
https://doi.org/10.1016/j.eswa.2020.113943

has been cited by the following article:

• TITLE: Explicit Artificial Intelligence Timetable Generator for Colleges and Universities

  AUTHORS: Lawrence A. Farinola, Mahougnon B. M. Assogba

  KEYWORDS: Genetic Algorithm, AI Timetabling, Automatic Scheduling, Constraint Satisfaction, Optimization

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.8, August 15, 2025

  ABSTRACT: Manual timetable preparation in colleges and universities is often time-consuming, error-prone, and inefficient, especially with increasing student and course complexity. This paper proposes an Explicit Artificial Intelligence-based Timetable Generator that leverages a Genetic Algorithm to automatically generate conflict-free, optimized academic schedules. The system incorporates user-defined constraints and priorities such as room availability, lecturer-course assignments, and course clash avoidance. Extensive testing was conducted using real-world institutional data to evaluate the system’s effectiveness. The proposed approach significantly reduced scheduling time, eliminated course conflicts, and improved allocation efficiency across multiple views, including per lecturer, per student, and per classroom. The results demonstrate the system’s scalability and practical viability for higher education environments.