Optimize class time tabling by using genetic algorithm technique in UTHM - Ahmad, Izah Rafidah (2019)
Timetable scheduling in academic institutions is a major challenge for the institutions, especially with a large number of students and courses offered. This becomes more challenging when classrooms are limited and needs to consider the meeting time of students with the lecturers. These academic institutions such as schools, colleges and universities need timetables to make sure that the students have enough time for each subject in a week without clashing with other subjects or other classes. There are elements that need to be considered in order to make a timetable. These elements include students, teachers or lecturers, rooms, period and also the subjects involved. A new branch of university which is Universiti Tun Hussein Onn Malaysia (UTHM) Pagoh will also have a problem to schedule timetables. Since the branch is new, therefore the problem of lacking in facilities, the number of classrooms and the number of students or classes will arise. In order to schedule timetables, reshuffling and arranging classrooms need to be done and may lead to the complexity of classrooms scheduling. In existing research, many problems involving scheduling have been solved by using genetic algorithm method. There are many other methods that were also being used such as linear programming, integer linear programming, tabu search, ant colony optimization (ACO) algorithm and goal programming. This research is about optimization problem and it proposes a heuristic approach for timetabling optimization, in order to improve and enhance the efficiency of classroom planning. A new algorithm was produced to handle the timetabling problem in the university. This research used genetic algorithm (GA) that was applied to java programming languages with a goal of reducing conflict and optimizing the fitness. Therefore, the general problem was being solved and the best solutions were obtained with lower number of conflicts and maximum fitness value. The timetables for the FAST firstyear students from the Mathematics Department and Statistics Department were also being solved with less conflict and maximum fitness value. A further analysis was done and the results provided the best solutions as well. This research gives an idea about timetable scheduling and also about the optimization method of GA. This research can also become a reference for other timetable scheduling.