Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)

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Optimizing Timetable Generation for Educational Institute Using Genetic Algorithm

Authors
Virti Shah1, *, Aastha Gadhvi1, Neel Oza1, Monali Sankhe1, Monika Mangla1
1Information Technology, DJSCE, Mumbai, India
*Corresponding author. Email: virti1709@gmail.com
Corresponding Author
Virti Shah
Available Online 26 May 2025.
DOI
10.2991/978-94-6463-716-8_37How to use a DOI?
Keywords
Hereditary calculation; Dynamic Principles; Rule based specialists; asset planning; heuristic calculations; Genetic Algorithm
Abstract

The timetable generation process is a critical component of educational institutions, characterized by its inherent complexity and NP-hard nature due to a multitude of constraints. This paper provides a comprehensive comparative analysis of various methods employed in timetable generation, with a particular focus on Genetic Algorithms (GAs). GAs offer a promising approach to solving combinatorial optimization problems efficiently, which makes them well suited for addressing the challenges of timetable scheduling. We evaluate the performance of GA-based methods in comparison with heuristic algorithms, examining key factors such as solution quality, computational efficiency, and scalability. By doing so, we aim to provide valuable insights into the effectiveness and suitability of GA-based strategies for timetable generation systems. This study highlights the strengths and limitations of GAs in handling the intricacies of scheduling tasks, contributing to a deeper understanding of their potential in overcoming the challenges inherent in timetable generation. The findings offer significant implications for future research and practical applications in this domain.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
26 May 2025
ISBN
978-94-6463-716-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-716-8_37How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Virti Shah
AU  - Aastha Gadhvi
AU  - Neel Oza
AU  - Monali Sankhe
AU  - Monika Mangla
PY  - 2025
DA  - 2025/05/26
TI  - Optimizing Timetable Generation for Educational Institute Using Genetic Algorithm
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 469
EP  - 481
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_37
DO  - 10.2991/978-94-6463-716-8_37
ID  - Shah2025
ER  -