Abstract:
This thesis focuses on balancing instructor workload and maximizing preferences by using the data in the first semester of 2019 from Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University as a case study. Since there are many instructors with over-workload in the department which directly affect their research qualities, balancing teaching workload is the main objective of this study. The proposed approach to balance workload is to split some basic courses into two parts: before midterm and after midterm and then assign each course to two instructors. Moreover, the preferences or the requests of teaching a course are important to maintain the instructor comfortable, and the preferences or the requests of teaching a course also help students to gain knowledge to their full potentials. The results show that our model is able to reduce both the number of instructors who have the high difference of requested workload and assigned workload, and the number of non-preferable courses for each instructor by comparing our results with the department timetable and the model without splitting courses.
