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Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil

The research endeavors to develop a web-based system specifically designed to create personalized university timetables for Universiti Teknologi MARA (UiTM) students using genetic algorithms, aiming to address the urgent need for a customizable timetable solution catering to the diverse scheduling r...

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Main Authors: Jamli, Mohd Radhi Fauzan, Ahmad Fadzil, Ahmad Firdaus
Format: Article
Language:English
Published: College of Computing, Informatics, and Mathematics 2024
Subjects:
Integer programming
Online Access:https://ir.uitm.edu.my/id/eprint/106030/1/106030.pdf
https://ir.uitm.edu.my/id/eprint/106030/
https://fskmjebat.uitm.edu.my/pcmj/
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spelling my.uitm.ir.1060302025-02-25T08:22:52Z https://ir.uitm.edu.my/id/eprint/106030/ Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil Jamli, Mohd Radhi Fauzan Ahmad Fadzil, Ahmad Firdaus Integer programming The research endeavors to develop a web-based system specifically designed to create personalized university timetables for Universiti Teknologi MARA (UiTM) students using genetic algorithms, aiming to address the urgent need for a customizable timetable solution catering to the diverse scheduling requirements of both repeater and non-repeater students while optimizing course group selection to minimize conflicts and enhance scheduling flexibility. The complexity of timetable generation stems from the varied course groupings and scheduling constraints inherent in UiTM's curriculum, leading to challenges for students, particularly repeaters, in enrolling in courses across different semesters and groupings, resulting in conflicts and inefficiencies. Traditional methods of timetable generation lack the adaptability needed to tackle these complexities, necessitating the development of an innovative solution. The proposed approach utilizes genetic algorithms to dynamically produce optimized timetables based on individual student needs, with real-time data scraping from 'iCRESS' ensuring the system stays up to date with the latest course information for accurate timetable generation. Within the genetic algorithm framework, each timetable is represented as a chromosome, forming a population of potential timetables refined through successive generations by genetic operators like crossover and mutation. Student input initiates the process, with user interaction allowing for timetable customization based on personal preferences. Extensive experimentation with genetic algorithm parameters has yielded promising results, notably a parameter set (population size = 12, generation size = 30, mutation rate = 0.2) demonstrating robust performance, achieving optimal timetables with swift convergence and minimal conflicts. This configuration excelled in efficiency and scalability, offering a viable solution for timetable generation at scale. Future work entails enhancing system robustness through comprehensive contingency planning, real-time data integration, and algorithmic optimization, with a focus on refining the genetic algorithm and exploring parallel processing techniques to further enhance efficiency and scalability. College of Computing, Informatics, and Mathematics 2024-10 Article NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/106030/1/106030.pdf Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil. (2024) Progress in Computer and Mathematics Journal (PCMJ) <https://ir.uitm.edu.my/view/publication/Progress_in_Computer_and_Mathematics_Journal_=28PCMJ=29/>, 1. pp. 528-544. ISSN 3030-6728 (Submitted) https://fskmjebat.uitm.edu.my/pcmj/
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Integer programming
spellingShingle Integer programming
Jamli, Mohd Radhi Fauzan
Ahmad Fadzil, Ahmad Firdaus
Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil
description The research endeavors to develop a web-based system specifically designed to create personalized university timetables for Universiti Teknologi MARA (UiTM) students using genetic algorithms, aiming to address the urgent need for a customizable timetable solution catering to the diverse scheduling requirements of both repeater and non-repeater students while optimizing course group selection to minimize conflicts and enhance scheduling flexibility. The complexity of timetable generation stems from the varied course groupings and scheduling constraints inherent in UiTM's curriculum, leading to challenges for students, particularly repeaters, in enrolling in courses across different semesters and groupings, resulting in conflicts and inefficiencies. Traditional methods of timetable generation lack the adaptability needed to tackle these complexities, necessitating the development of an innovative solution. The proposed approach utilizes genetic algorithms to dynamically produce optimized timetables based on individual student needs, with real-time data scraping from 'iCRESS' ensuring the system stays up to date with the latest course information for accurate timetable generation. Within the genetic algorithm framework, each timetable is represented as a chromosome, forming a population of potential timetables refined through successive generations by genetic operators like crossover and mutation. Student input initiates the process, with user interaction allowing for timetable customization based on personal preferences. Extensive experimentation with genetic algorithm parameters has yielded promising results, notably a parameter set (population size = 12, generation size = 30, mutation rate = 0.2) demonstrating robust performance, achieving optimal timetables with swift convergence and minimal conflicts. This configuration excelled in efficiency and scalability, offering a viable solution for timetable generation at scale. Future work entails enhancing system robustness through comprehensive contingency planning, real-time data integration, and algorithmic optimization, with a focus on refining the genetic algorithm and exploring parallel processing techniques to further enhance efficiency and scalability.
format Article
author Jamli, Mohd Radhi Fauzan
Ahmad Fadzil, Ahmad Firdaus
author_facet Jamli, Mohd Radhi Fauzan
Ahmad Fadzil, Ahmad Firdaus
author_sort Jamli, Mohd Radhi Fauzan
title Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil
title_short Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil
title_full Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil
title_fullStr Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil
title_full_unstemmed Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil
title_sort web based personalized university timetable for uitm students using genetic algorithm / mohd radhi fauzan jamli and ahmad firdaus ahmad fadzil
publisher College of Computing, Informatics, and Mathematics
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/106030/1/106030.pdf
https://ir.uitm.edu.my/id/eprint/106030/
https://fskmjebat.uitm.edu.my/pcmj/
_version_ 1825165088303087616
score 13.239859

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