Optimizing teaching allocation with preparation time constraint: a linear programming approach
The process of allocating optimal teaching workload among lecturers is intricate. Various work requirements must be considered and balanced apart from teaching duties, such as research, publications, and administrative obligations. Traditionally, allocation of teaching load considers the number of c...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
GADING Journal of Science and Technology
2024
|
| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/115469/1/115469.pdf https://ir.uitm.edu.my/id/eprint/115469/ https://myjms.mohe.gov.my/index.php/gjst/article/view/27436/15521 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The process of allocating optimal teaching workload among lecturers is intricate. Various work requirements must be considered and balanced apart from teaching duties, such as research, publications, and administrative obligations. Traditionally, allocation of teaching load considers the number of courses, groups, and teaching hours without factoring the preparation time. However, with larger group size, preparation time increases significantly. This study introduces a new constraint to reflect the preparation time factor in the teaching load allocation problem. Using the linear programming model, nine courses and a total of 37 groups are to be distributed among nine lecturers, projected for the October 2024 semester. The model considers all the minimum and maximum requirements for each lecturer, and accounts for group size when estimating the preparation time. Then, the model is solved using an online optimization solver, NEOS Server. Results revealed an optimal teaching load distribution satisfying all seven constraints, including the preparation time factor. This model provides a better allocation of teaching duties, suggesting future improvements could be refined by examining how the preparation time scales with increased assignment of courses. Overall, this study proposed a practical approach for a more balanced teaching load distribution considering preparation time, facilitating a better management of lecturer workloads. |
|---|