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Applying Genetic Algorithms for Dynamic Timetable Generation and Optimization

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Applying Genetic Algorithms for Dynamic Timetable Generation and Optimization


Saloni Kanhekar



Saloni Kanhekar "Applying Genetic Algorithms for Dynamic Timetable Generation and Optimization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Smart Innovations in Computer Science and Applications, March 2026, pp.566-579, URL: https://www.ijtsrd.com/papers/ijtsrd101643.pdf

Academic timetables development is an intricate combinatorial problem to solve to a greater extent that schools have for every academic session. Traditional manual scheduling methods with spreadsheets become unhelpful due to their failure to account for scale and inaccuracy with the institution's complexity and typically result in resource conflicts, teacher overlaps, and uneven workload distribution. In this work, we present a smart, automated timetable generation system using the principles of evolutionary computation to deal with this NP-hard scheduling problem. The architecture of the system integrates a GA (Genetic Algorithm) that aims to provide satisfaction around multi-constraints including hard constraints (e.g. overlaps between teacher unavailability, room conflicts and capacity limitations) and soft constraints to improve schedule quality. It’s a full-stack web app made using Python Django and featuring a modular layout to take care of teachers, courses, departments, sections, rooms, and time slots. We are interested in the Genetic Algorithm, which will employ tournament selection, single-point crossover, and random mutation operators to adapt optimal timetable solutions generationally. Results show that the system can generate conflict-free schedules in 50-100 generations at a 100% hard constraint satisfaction; it has the ability to optimize for soft constraints. This implementation achieves an 85% time reduction in timetable preparation compared to manual approaches and a scalable architecture fit for institutions that operate with multiple departments or diverse academic structures.

Timetable Generation; Genetic Algorithm; Evolutionary Computation; Constraint Satisfaction; NP-Hard Optimization; Django Framework; Automated Scheduling; Educational Administration


IJTSRD101643
Special Issue | Smart Innovations in Computer Science and Applications, March 2026
566-579
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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