Evaluate the performance of university timetabling problem with various artificial intelligence techniques
University timetabling is a complex and critical task in higher education institutions as it involves the assignment of courses, lecturers, and students to available timeslots and venues while satisfying various constraints. The project focuses on developing an automated university course timetable...
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| Format: | Final Year Project / Dissertation / Thesis |
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2025
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| Online Access: | http://eprints.utar.edu.my/7090/1/fyp_CS_2025_CHWY.pdf http://eprints.utar.edu.my/7090/ |
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| _version_ | 1854094470641876992 |
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| author | Hooi, Charmaine Wai Yee |
| author_facet | Hooi, Charmaine Wai Yee |
| author_sort | Hooi, Charmaine Wai Yee |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | University timetabling is a complex and critical task in higher education institutions as it involves the assignment of courses, lecturers, and students to available timeslots and venues while satisfying various constraints. The project focuses on developing an automated university course timetable scheduling tool using Genetic Algorithm (GA). University course timetable scheduling (UCTTP) is a well-known optimization problem due to its NP-hard nature and the complexity of the problem increases exponentially with eh addition of constraints. Over time, numerous algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and other approaches have been introduced to address the challenges of optimizing class schedules. While each university or institution have its own unique constraints, this project aims to improve existing timetabling systems by introducing a new constraint, the ‘Proximity and Travel Minimization Constraint’ which optimizes class schedules to minimize travel distances between venues scheduled in adjacent time slots. By implementing this new constraint, the project addresses the gap in traditional timetabling methods, which often overlook the impact of travel distances on the efficiency and experience of both lecturers and students. Hence, through the application of GA, this project aims to develop an efficient university class timetabling tool that integrates the newly introduced constraint. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.7090 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.70902025-12-28T15:10:54Z Evaluate the performance of university timetabling problem with various artificial intelligence techniques Hooi, Charmaine Wai Yee T Technology (General) University timetabling is a complex and critical task in higher education institutions as it involves the assignment of courses, lecturers, and students to available timeslots and venues while satisfying various constraints. The project focuses on developing an automated university course timetable scheduling tool using Genetic Algorithm (GA). University course timetable scheduling (UCTTP) is a well-known optimization problem due to its NP-hard nature and the complexity of the problem increases exponentially with eh addition of constraints. Over time, numerous algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and other approaches have been introduced to address the challenges of optimizing class schedules. While each university or institution have its own unique constraints, this project aims to improve existing timetabling systems by introducing a new constraint, the ‘Proximity and Travel Minimization Constraint’ which optimizes class schedules to minimize travel distances between venues scheduled in adjacent time slots. By implementing this new constraint, the project addresses the gap in traditional timetabling methods, which often overlook the impact of travel distances on the efficiency and experience of both lecturers and students. Hence, through the application of GA, this project aims to develop an efficient university class timetabling tool that integrates the newly introduced constraint. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7090/1/fyp_CS_2025_CHWY.pdf Hooi, Charmaine Wai Yee (2025) Evaluate the performance of university timetabling problem with various artificial intelligence techniques. Final Year Project, UTAR. http://eprints.utar.edu.my/7090/ |
| spellingShingle | T Technology (General) Hooi, Charmaine Wai Yee Evaluate the performance of university timetabling problem with various artificial intelligence techniques |
| title | Evaluate the performance of university timetabling problem with various artificial intelligence techniques |
| title_full | Evaluate the performance of university timetabling problem with various artificial intelligence techniques |
| title_fullStr | Evaluate the performance of university timetabling problem with various artificial intelligence techniques |
| title_full_unstemmed | Evaluate the performance of university timetabling problem with various artificial intelligence techniques |
| title_short | Evaluate the performance of university timetabling problem with various artificial intelligence techniques |
| title_sort | evaluate the performance of university timetabling problem with various artificial intelligence techniques |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/7090/1/fyp_CS_2025_CHWY.pdf http://eprints.utar.edu.my/7090/ |
| url_provider | http://eprints.utar.edu.my |