University schedule generator

In the realm of educational institutions, challenging scheduling issues is course timetabling. The quest for an optimal schedule revolves around harmoniously meshing resources such as educators, subjects, students, and classrooms while avoiding conflicts and adhering to a multitude of essential and...

Full description

Saved in:
Bibliographic Details
Main Authors: Chougule, Samiksha, Ghuge, Krishna, Kote, Abhishek, Ramteke, Ashwin
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Artificial intelligence
Colleges & universities
Machine learning
Resource scheduling
Schedules
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the realm of educational institutions, challenging scheduling issues is course timetabling. The quest for an optimal schedule revolves around harmoniously meshing resources such as educators, subjects, students, and classrooms while avoiding conflicts and adhering to a multitude of essential and preferential constraints. This paper explores the application of various artificial intelligence (AI) and Machine Learning (ML) algorithms as a promising approach to tackle the intricate university timetabling problem. We endeavor to provide essential context and motivation for harnessing ML techniques in the development of a schedule generator. We conduct a comprehensive review of significant works, shedding light on the evolving landscape of academic timetabling solutions.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0227935