Solving an application of university course timetabling problem by using genetic algorithm

Generating timetables for academic institutions is a complex problem. This is due to many constraints involved whether they are vital or desirable, which are known as hard and soft constraints. The problem becomes more complicated and difficult to solve as the number of courses increase. Moreover, g...

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Main Author:	Norhana, Shaibatul Khadri
Format:	Thesis
Language:	eng eng
Published:	2022
Subjects:	QA299.6-433 Analysis
Online Access:	https://etd.uum.edu.my/10137/1/s821004_01.pdf https://etd.uum.edu.my/10137/2/s821004_02.pdf
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id	my-uum-etd.10137
record_format	uketd_dc
spelling	my-uum-etd.101372022-12-14T08:33:31Z Solving an application of university course timetabling problem by using genetic algorithm 2022 Norhana, Shaibatul Khadri Abdul Rahman, Rosshairy Abdul Rahman, Syariza Awang Had Salleh Graduate School of Arts & Sciences Awang Had salleh Graduate School of Art & Sciences QA299.6-433 Analysis Generating timetables for academic institutions is a complex problem. This is due to many constraints involved whether they are vital or desirable, which are known as hard and soft constraints. The problem becomes more complicated and difficult to solve as the number of courses increase. Moreover, generating manual timetables is challenging and time-consuming, particularly to meet lecturers’ preferences. Thus, it is crucial to establish an automated course timetable system. Many efforts have been made using various computational heuristic methods to acquire the best solutions. Among the approaches, genetic algorithm (GA), constructed based on Darwin's theory of evolution, becomes the renowned approach to solve various types of timetabling problems. Therefore, this study produces the best timetable using GA to solve clashed courses, optimize room utilization and maximize lecturers’ preferences. Data of 41 course sections from 17 courses offered in semester A172 were taken from Decision Science Department, School of Quantitative Sciences (SQS). The phases in GA involves a number of main operators which are population initialization, crossover and mutation. The best parameter setting for GA was determined through combination of different mutation rate, population and iteration. The simulation results of GA show that this method is able to produce the best fitness value that satisfied all hard and soft constraints. There are no clashes either between lecturers or lecture rooms, and lecturers’ preferences were satisfied. The system can help SQS or any other academic schools or institutions to easily develop course timetabling in the coming semesters. 2022 Thesis https://etd.uum.edu.my/10137/ https://etd.uum.edu.my/10137/1/s821004_01.pdf text eng 2025-09-12 staffonly https://etd.uum.edu.my/10137/2/s821004_02.pdf text eng public other masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Abdul Rahman, Rosshairy Abdul Rahman, Syariza
topic	QA299.6-433 Analysis
spellingShingle	QA299.6-433 Analysis Norhana, Shaibatul Khadri Solving an application of university course timetabling problem by using genetic algorithm
description	Generating timetables for academic institutions is a complex problem. This is due to many constraints involved whether they are vital or desirable, which are known as hard and soft constraints. The problem becomes more complicated and difficult to solve as the number of courses increase. Moreover, generating manual timetables is challenging and time-consuming, particularly to meet lecturers’ preferences. Thus, it is crucial to establish an automated course timetable system. Many efforts have been made using various computational heuristic methods to acquire the best solutions. Among the approaches, genetic algorithm (GA), constructed based on Darwin's theory of evolution, becomes the renowned approach to solve various types of timetabling problems. Therefore, this study produces the best timetable using GA to solve clashed courses, optimize room utilization and maximize lecturers’ preferences. Data of 41 course sections from 17 courses offered in semester A172 were taken from Decision Science Department, School of Quantitative Sciences (SQS). The phases in GA involves a number of main operators which are population initialization, crossover and mutation. The best parameter setting for GA was determined through combination of different mutation rate, population and iteration. The simulation results of GA show that this method is able to produce the best fitness value that satisfied all hard and soft constraints. There are no clashes either between lecturers or lecture rooms, and lecturers’ preferences were satisfied. The system can help SQS or any other academic schools or institutions to easily develop course timetabling in the coming semesters.
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Norhana, Shaibatul Khadri
author_facet	Norhana, Shaibatul Khadri
author_sort	Norhana, Shaibatul Khadri
title	Solving an application of university course timetabling problem by using genetic algorithm
title_short	Solving an application of university course timetabling problem by using genetic algorithm
title_full	Solving an application of university course timetabling problem by using genetic algorithm
title_fullStr	Solving an application of university course timetabling problem by using genetic algorithm
title_full_unstemmed	Solving an application of university course timetabling problem by using genetic algorithm
title_sort	solving an application of university course timetabling problem by using genetic algorithm
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2022
url	https://etd.uum.edu.my/10137/1/s821004_01.pdf https://etd.uum.edu.my/10137/2/s821004_02.pdf
_version_	1776103749582848000

Solving an application of university course timetabling problem by using genetic algorithm

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