Automated Student Timetable Scheduling System based on Genetic Algorithm - Amirul Azuani, Romlee; Noor Ain, Rosly; Meng, Chuan Haw (INTI International University, 2019)
Timetable scheduling is always a major challenge in an education setting due to the complexity of the timetabling environment including large number of students, changing in study plan, diverse courses and limited number of classrooms. These factors lead to several major problems identified during the enrollment period such as timetable clashing between students, continuous hours of lecture and difficulty to find a matching slot for the clashed courses. In order to solve these problems, automated timetable scheduling system based on genetic algorithm is proposed which is estimated to reduce the chances of class clashing and prevent continuous lecture time. Genetic algorithm works based on natural evolutions that comprises of several iterations. The iterations will continue to generates new generation cycles until the optimum schedule is met. By using minimal data entry such as the course information, the system will be able to find the best timeslot. As a result, student and lecturer will have some gap between their lecturer hour while the classroom and laboratory can be assigned more effectively as well as classroom clashing which continuously happened in the manual scheduling system can be avoided. Hence, genetic algorithm will be able to find a better solution for timetable scheduling as well as optimizing the efficiency of the timetabling unit in INTI International University.