Generation of timetables using genetic algorithms

Keywords: Genetic algorithm, school timetable, optimization

Abstract

In the present work, a school time table is obtained for the educational program Bachelor's Degree in Information Technology of Escuela Superior de Tizayuca through a genetic algorithm, this program was carried out since, despite the fact that there are various works in this regard, each school has a specific way to work, for which a program focused on these needs is needed. The program uses two types of constraints, essential and non-essential. It is able to obtain a useful time table as it does not violate any essential constraints and only violates non-essential constraints twice. This program can serve as a base to create another one that obtains the schedules of the whole school. Obtaining optimized school time tables allows for more efficient use of resources, such as classrooms and laboratories, it also makes the work of administrative and teaching staff more efficient, more so in the latter, since it reduces the mental and physical wear and tear on teachers that they sometimes have a lot of hours without showing classes.

Downloads

Download data is not yet available.

References

Arora, R. Optimization: Algorithms and Applications. CRC Press. 2015.

Georgiadis, G., Elekidis, A., & Georgiadis, M. Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications. Processes. 2019; 7(7).

Rao, S. Engineering Optimization: Theory and Practice. New Age International. 2013.

Kramer, O. Genetic Algorithm Essentials. Springer International Publishing. 2017.

Wirsansky, E. Hands-On Genetic Algorithms with Python. Packt Publishing Limited. 2020.

Waschka II, R. Composing with Genetic Algorithms: GenDash. In Evolutionary Computer Music. Springer London, 2007: 117–136

Alghamdi, H.; Alsubait, T.; Alhakami, H.; Baz, A. A Review of Optimization Algorithms for University Timetable Scheduling. Eng. Technol. Appl. Sci. Res. 2020; 10: 6410-6417.

Jacobson, L., & Kanber, B. Genetic Algorithms in Java Basics. Apress. 2015.

Eiben, A., & Smit, S. Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation. 2011; 1: 19–31.

Published
2022-12-05
How to Cite
Acuña-Galván, I., Lezama León, E., Bolaños-Rodríguez, E., Solís-Galindo, A. E., & Vega-Cano, G. Y. (2022). Generation of timetables using genetic algorithms. Boletín Científico INVESTIGIUM De La Escuela Superior De Tizayuca, 8(Especial), 51-57. https://doi.org/10.29057/est.v8iEspecial.9866