ISSN:2582-5208

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Paper Key : IRJ************254
Author: Nikita Aghicha,Prof. Sana Shaikh,Aditya Deshmane ,Krishna Suralkar,Atharva Koshti
Date Published: 17 Nov 2024
Abstract
This project aims to develop a robust timetable generator that combines artificial intelligence (AI) techniques and genetic algorithms (GA) to simplify and optimize academic scheduling. Leveraging AI language models, the system autonomously gathers university-specific data and rule-based constraints, ensuring each timetable meets institutional requirements. The GA then optimizes the AI-generated schedule by refining it through iterative processes, producing a final timetable that minimizes conflicts and maximizes resource allocation. This dual approach provides academic institutions with an efficient, scalable solution that requires minimal manual intervention.
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