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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| Language: | en en |
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2022
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| 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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| author | Norhana, Shaibatul Khadri |
| author_facet | Norhana, Shaibatul Khadri |
| author_sort | Norhana, Shaibatul Khadri |
| building | UUM Library |
| collection | Institutional Repository |
| content_provider | Universiti Utara Malaysia |
| content_source | UUM Electronic Theses |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.uum.etd-10137 |
| institution | Universiti Utara Malaysia |
| language | en en |
| publishDate | 2022 |
| record_format | eprints |
| 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. |
| spellingShingle | QA299.6-433 Analysis Norhana, Shaibatul Khadri Solving an application of university course timetabling problem by using genetic algorithm |
| title | 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_short | 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 |
| topic | QA299.6-433 Analysis |
| 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/ |
| url_provider | http://etd.uum.edu.my/ |