2025, Vol. 6, Issue 2, Part C
Optimizing primary school timetables using genetic algorithms: A real-world case study
Author(s): Qusay Issam Hamid
Abstract: Timetable generation in primary schools presents a complex combinatorial optimization challenge, often constrained by teacher availability, classroom capacity, and curriculum requirements. Traditional manual approaches are time-consuming and frequently lead to scheduling conflicts. This study proposes a Genetic Algorithm (GA)-based model to automate and enhance the class timetable construction process. The model was tested on real data collected from a public primary school, incorporating essential scheduling constraints. Results demonstrate a significant reduction in scheduling conflicts and a 98% decrease in time consumption compared to manual methods. The findings confirm the effectiveness of evolutionary computation in solving NP-hard problems in educational contexts and support its broader adoption in school management systems.
DOI: https://doi.org/10.22271/math.2025.v6.i2c.238
Pages: 467-472 | Views: 153 | Downloads: 82
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How to cite this article:
Qusay Issam Hamid. Optimizing primary school timetables using genetic algorithms: A real-world case study. Journal of Mathematical Problems, Equations and Statistics. 2025; 6(2): 467-472. DOI: 10.22271/math.2025.v6.i2c.238
