Computerised Faculty Course Timetabling with Student Sectioning
This research focuses on a faculty course timetabling at the Faculty of Computer Science and Information Technology (FCSlT), Universiti Malaysia Sarawak (UNIMAS). In this case study, course pre-registration is not a practice. Therefore, there is no precise estimation on course registration and cause...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English English |
| Published: |
2016
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/37881/1/Bong%20Chia%20Lih%2024pgs.pdf http://ir.unimas.my/id/eprint/37881/8/Bong%20Chia%20Lih%20ft.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This research focuses on a faculty course timetabling at the Faculty of Computer Science and Information Technology (FCSlT), Universiti Malaysia Sarawak (UNIMAS). In this case study, course pre-registration is not a practice. Therefore, there is no precise estimation on course registration and causes faculty's experienced planners to arrange the timetable by curriculum-based. Curriculum-based timetable will create a lot of changes after the semester has started, where "repeaters" students tend to face clashes in their course timetable. Besides, students are increasing consistently from semester to semester although the number of venue resources remains the same. Due to all these issues, the objective of this study is to develop a computerised aIgoritrull to minimise the clashes issue and increase venue utilisation. A two-stage heuristic method is proposed to solve the faculty course timetabJing problem by student-based. Data pre-processing algorithm was carried out to predict course registration based on students' curriculum course plan and previous examination result. "Repeaters" are taken into account in order to solve the problem comprehensively. The two-stage heuristic method divided courses into course groups in first stage to ease the timeslot-venue allocation in the second stage. Student sectioning was also considered to solve venue inadequacy by dividing large size courses into several course sections. Different course sections will have lecture in different venues either during the same or different timeslots. The simulator was tested with three real semesters' data from FCSIT. Three data sets were varied in problem size. The computational time of the simulator ranging from less than minute to about 10 minutes depend on the problem instances. All the timetable solutions generated by the simulator are no-clash solution with minimum unallocated courses. In ten11 of venue utilisation, two-stage heuristic solution manages to allocate exactly with the demand up to 98% but real solution can perfon11 best at only 75%. For timetable distribution, statistic graphs based on student clusters have been plotted on number of lecture days per week, number of lecture hours in a timetable day and number of highest continuous lecture hours in a timetable day. On average, 86% timetables have more than three lecture days per week, more than 93% of timetable days have acceptable range of 5 lecture hours and more than 90% of timetable days have less than 5 highest continuous hours. Moreover, two-stage heuristic timetabling also provides the convenience in editing the data attached to the problem and regenerate solutions in seconds. The computational result demonstrates that proposed heul;stic algorithm outperfoJ111 current practices in all datasets. A sets of sensitivity analyses have been conducted with different 'scenario of student size, number of timeslots and number of venues. All the sensitivity analyses results demonstrate that the proposed solution is effective and robust in solving FCSIT course timetabling
problem for different scenario. |
|---|