A linear programming model for teaching load allocation of academic staff at University / Nur Syahirah Ibrahim

• Main Author: Ibrahim, Nur Syahirah
• Format: Student Project
• Language: English
• Published: 2021
• Subjects: Personnel management; Analysis
• Online Access: https://ir.uitm.edu.my/id/eprint/49469/1/49469.pdf; https://ir.uitm.edu.my/id/eprint/49469/
• Institution: Universiti Teknologi Mara

This study that is about teaching loads refers to the allocation of teaching hours among academic staff. It will be the ideal way to assign the right courses to the right staff based on their expertise, preference, and experience. In many teaching units, the task is done manually through trial-and-error which is rather time-consuming, inefficient, and subject to bias judgment. The purpose of this study is to use linear programming to optimize the model and to appoint the right lecturer to teach the courses on the basis of their chosen weight. Therefore, a linear programming model was developed to achieve an optimum allocation of teaching loads subject to considered constraints. The importance of the research is that it is possible to assign a suitable lecturer to the right position, and does not need to be assigned manually. A survey was conducted to determine the preferences of academic staff to teach the courses offered where primary data was collected using Google Forms among 20 lecturers from the Department of Mathematics, UiTM Arau, Perlis with 26 courses considered, and secondary data from academic affairs was collected. A feasible solution was found with the use of optimization software LINGO version 18 optimization software and Excel 2020, where the objectives were achieved since the lecturers get to teach the courses based on their preference weights and the model is optimized by using the LP method. Therefore, the model serves as a good tool to assist the head of teaching units in allocating teaching loads and this will improve the quality of teaching as well as will provide a great impact on the students’ results.