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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:English
English
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
https://etd.uum.edu.my/10137/
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spelling my.uum.etd.101372022-12-14T08:33:31Z https://etd.uum.edu.my/10137/ Solving an application of university course timetabling problem by using genetic algorithm Norhana, Shaibatul Khadri 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 NonPeerReviewed text en https://etd.uum.edu.my/10137/1/s821004_01.pdf text en https://etd.uum.edu.my/10137/2/s821004_02.pdf Norhana, Shaibatul Khadri (2022) Solving an application of university course timetabling problem by using genetic algorithm. Masters thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
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
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
publishDate 2022
url https://etd.uum.edu.my/10137/1/s821004_01.pdf
https://etd.uum.edu.my/10137/2/s821004_02.pdf
https://etd.uum.edu.my/10137/
_version_ 1753791149148995584
score 13.244369

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