Explicit Artificial Intelligence Timetable Generator for Colleges and Universities

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.

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Publication Details

Journal
Open Journal of Applied Sciences
Published
2025-01-01
DOI
https://doi.org/10.4236/ojapps.2025.158151
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
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article

Explicit Artificial Intelligence Timetable Generator for Colleges and Universities

Open Journal of Applied Sciences
Geochemistry and Geologic Mapping
article

Explicit Artificial Intelligence Timetable Generator for Colleges and Universities

article en

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.

Open Journal of Applied SciencesVol. 15(08)
Russian Academy of Architecture and Construction Sciences (RU)
Openalex Percentile: Top 70%
Geochemistry and Geologic Mapping
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Explicit Artificial Intelligence Timetable Generator for Colleges and Universities · Open Journal of Applied Sciences (2025) | TGRS Research Map | TGRS