Journal of Information Technology and Digital World
IRO Journals
Volume 8 — Issue 1 — March 2026

Design of a Smart Timetable Generator Using Artificial Intelligence for NEP 2020 Multidisciplinary Learning Models

https://doi.org/10.36548/jitdw.2026.1.003
Pages: 26-44
PDF
Affrina Prince A.
Artificial Intelligence and Data Science, Bannari Amman Institute of Technology, Sathyamangalam, India
Gokul Rama Subbiah
Artificial Intelligence and Data Science, Bannari Amman Institute of Technology, Sathyamangalam, India
Sanjay V.
Artificial Intelligence and Data Science, Bannari Amman Institute of Technology, Sathyamangalam, India
March 2026
Volume 8 — Issue 1
Journal of Information Technology and Digital World
PDF

How to Cite

A., Affrina Prince, Gokul Rama Subbiah, and Sanjay V. 2026. “Design of a Smart Timetable Generator Using Artificial Intelligence for NEP 2020 Multidisciplinary Learning Models ”. Journal of Information Technology and Digital World 8 (1): 26-44. https://doi.org/10.36548/jitdw.2026.1.003.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Genetic Algorithm
— Machine Learning
— NEP 2020
— Timetable Generation
— Constraint Optimization
— Adaptive Parameter Tuning
Published: 20-03-2026

Abstract

Timetable generation is an extremely complicated problem of combinatorial optimization with numerous challenging limitations constraints. As a result, newly introduced requirements under the National Education Policy (NEP) 2020 for academic institutions (i.e., credit flexibility, multiple options for completion of courses across other disciplines and more dynamic schedules) has become complicated to generate a timetable. In this study, the research developed a newly adaptive GA (Genetic Algorithm) for the generation of a course schedule in a highly efficient and scalable machine-learning-based parameter modifications to implement NEP 2020 credit flexibility constraints and to modify both the probability of crossover and mutation dynamically based on the predicted velocity of the GA (Genetic Algorithm) to converge as indicated by a supervised learning-based model. The work developed the GA (Genetic Algorithm) to operate using Python for backend processes, React for user interface development and MySQL for data management. The experimental evaluation of a medium-sized dataset (3 departments, 96 courses, 54 instructors, 24 classrooms and 40 weekly time slots) indicates that the method resulted in better training convergence and execution than traditional GA-based solutions. Therefore, the methodology has the potential for increased speed of convergence, fewer constraint violations and is suitable for today's higher education scheduling environments.

References

  1. Burke, Edmund K., Jakub Mareček, Andrew J. Parkes, and Hana Rudová. "A SuperNnodal Formulation of Vertex Colouring with Applications in Course Timetabling." Annals of Operations Research 179, no. 1 (2010): 105-130.
  2. Abdullah, Salwani, Edmund K. Burke, and Barry Mccollum. "An investigation of variable neighbourhood search for university course timetabling." In The 2nd multidisciplinary international conference on scheduling: theory and applications (MISTA), (2005): 413-427.
  3. Qu, Rong, Edmund K. Burke, Barry McCollum, Liam TG Merlot, and Sau Y. Lee. "A survey of search methodologies and automated system development for examination timetabling." Journal of scheduling 12, no. 1 (2009): 55-89.
  4. Eiben, Agoston E., Jan K. Van Der Hauw, and Jano I. van Hemert. "Graph coloring with adaptive evolutionary algorithms." Journal of Heuristics 4, no. 1 (1998): 25-46.
  5. Carter, Michael W., and Gilbert Laporte. "Recent developments in practical course timetabling." In International conference on the practice and theory of automated timetabling, Berlin, Heidelberg: Springer Berlin Heidelberg, (1997): 3-19.
  6. Colorni, Alberto, Marco Dorigo, and Vittorio Maniezzo. "Metaheuristics for high school timetabling." Computational optimization and applications 9, no. 3 (1998): 275-298.
  7. Eiben, Ágoston E., Robert Hinterding, and Zbigniew Michalewicz. "Parameter control in evolutionary algorithms." IEEE Transactions on evolutionary computation 3, no. 2 (1999): 124-141.
  8. F. G. Lobo, C. F. Lima, and Z. Michalewicz, Parameter Setting in Evolutionary Algorithms, Springer, 2007.
  9. Jin, Yaochu. "A comprehensive survey of fitness approximation in evolutionary computation." Soft computing 9, no. 1 (2005): 3-12.
  10. Zhang, Qiang. "An optimized solution to the course scheduling problem in universities under an improved genetic algorithm." Journal of Intelligent Systems 31, no. 1 (2022): 1065-1073.
  11. Branke, Jürgen, and Hartmut Schmeck. "Designing evolutionary algorithms for dynamic optimization problems." In Advances in evolutionary computing: theory and applications, Berlin, Heidelberg: Springer Berlin Heidelberg, (2003): 239-262.
  12. Tang, Yuanjie, Rengkui Liu, Futian Wang, Quanxin Sun, and Amr A. Kandil. "Scheduling optimization of linear schedule with constraint programming." Computer‐Aided Civil and Infrastructure Engineering 33, no. 2 (2018): 124-151.
  13. Al-Betar, Mohammed Azmi, Ahamad Tajudin Khader, and Munir Zaman. "University course timetabling using a hybrid harmony search metaheuristic algorithm." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, no. 5 (2012): 664-681.
  14. Cevallos, Fabian, and Fang Zhao. "Minimizing transfer times in public transit network with genetic algorithm." Transportation Research Record 1971, no. 1 (2006): 74-79.
  15. Shabbir, Attia, Raja Hashim Ali, Muhammad Zeeshan Shabbir, Zain Ul Abideen, Talha Ali Khan, Ali Zeeshan Ijaz, Nisar Ali, Muhammad Imad, and Muhammad Abu Bakar. "Genetic algorithm-based feature selection for accurate breast cancer classification." In 2023 International Conference on IT and Industrial Technologies (ICIT), IEEE, (2023): 1-6.
  16. Abbas, Shaheer, Ahmad Maaz, Raja Hashim Ali, Talha Ali Khan, and Iftikhar Ahmed. "Automatic timetable generation using neural networks trained by genetic algorithms." In 2024 18th International Conference on Open Source Systems and Technologies (ICOSST), IEEE, (2024): 1-6.
  17. Solotorevsky, Gadi, Ehud Gudes, and A. Meisels. "Algorithms for Solving Distributed Constraint Satisfaction Problems (DCSPs)." In AIPS, (1996): 191-198.
  18. Yokoo, Makoto. "Asynchronous weak-commitment search for solving distributed constraint satisfaction problems." In International Conference on Principles and Practice of Constraint Programming, Berlin, Heidelberg: Springer Berlin Heidelberg, (1995): 88-102.
  19. Neiman, Daniel E., and Victor R. Lesser. "A Cooperative Repair Method for a Distributed Scheduling System." In AIPS, (1996): 166-173.