Optimizing multi-objective lecturer-to-course assignments using the modified Hungarian method: Balancing competency and preferences

Nur Syahirah Ibrahim; Adibah Shuib; Zati Aqmar Zaharudin.

Efficiently assigning lecturers to courses is a critical aspect of ensuring both faculty satisfaction and optimal teaching outcomes in Higher Education Institutions. This study introduces an innovative Modified Hungarian Method (MHM) optimization model to address this challenge by incorporating lecturers’ competency scores and preference levels. While previous studies have primarily utilized the traditional Hungarian Method, limited attention has been given to its modified counterpart. Additionally, the application of competency and preference-based criteria in lecturer-to-course assignments remains unexplored. To address these gaps, this research develops a mathematical programming approach to enhance the formulation of the MHM model. The proposed model, referred to as the Competency-Preference Multi-Objective MHM (CP MO-MHM), seeks to achieve two main objectives, maximizing lecturers’ competencies and maximizing their preferences in course assignments. Competency is evaluated through three dimensions that are knowledge, skills and teaching motivation. Data for this study were collected via an online survey of Mathematics lecturers teaching undergraduate courses at UiTM Shah Alam, Malaysia. Using the gathered competency scores and preference levels, the CP MO-MHM model was implemented in MATLAB’s intlinprog function to generate an optimal lecturer-to-course assignment plan, with a maximum limit of three courses per lecturer. The results demonstrate that the CP MO-MHM model effectively identifies the most suitable course assignments for lecturers based on their competencies and preferences. By adapting the MHM framework to integrate these multidimensional inputs, this study contributes a practical tool for improving educational planning. The model not only enhances teaching quality but also minimizes mismatches between lecturers and courses, promoting better academic performance and greater satisfaction among faculty members. This research offers significant advancements in lecturer assignment processes, paving the way for more efficient and effective resource management in academia.

KEYWORDS: Competency; Preferences; Lecturers-to-Courses Assignment; Mathematical Programming; Modified Hungarian Method



Download this PDF file

REFERENCES

1. Abdul Latip, M. S., Newaz, F. T. & Ramasamy, R. 2020. Students’ Perception of Lecturers’ Competency and the Effect on Institution Loyalty: The Mediating Role of Students’ Satisfaction. Asian Journal of University Education, 16(2), 183.
2. Ahmed, S. S., Mariga, U. N., Abdulmalik, S., Ango, I. S. & Umaru, S. 2022. The Use of Assignment Problem Models to Assign Teachers to Classes: A Case Study of Ado Bobi Primary School. Transactions of the Nigerian Association of Mathematical Physics, 18, 167–172.
3. Ge, S., Cheng, M., He, X. & Zhou, X. 2020. A Two-Stage Service Migration Algorithm in Parked Vehicle Edge Computing for Internet of Things. Sensors, 20(10), 2786.
4. Hair, J. & Page, M. 2015. The Essentials of Business Research Methods (3rd ed.). New York Routledge.
5. Ibrahim, N. S., Shuib, A. & Zaharudin, Z. A. 2024. Modified Hungarian model for lecturer-to-course assignment. AIP Conference Proceedings, 2850, 080001.
6. Kabiru, S., Saidu, B. M., Abdul, A. Z. & Ali, U. A. 2017. An Optimal Assignment Schedule of Staff-Subject Allocation. Journal of Mathematical Finance, 07(04), 805–820.
7. Kuhn, H. W. 1955. The Hungarian Method for the Assignment Problem. Naval Research Logistics Quarterly, 2(1–2), 83–97.
8. Liu, Y., Liu, K., Han, J., Zhu, L., Xiao, Z. & Xia, X.-G. 2021. Resource Allocation and 3-D Placement for UAV-Enabled Energy-Efficient IoT Communications. IEEE Internet of Things Journal, 8(3), 1322–1333.
9. Mukherjee, P. & De, T. 2023. Interference aware D2D Multicasting using Modified Hungarian Method. 2023 OITS International Conference on Information Technology (OCIT). 13-15 December 2023. Raipur, India. pp 319–324.
10. Solaja, O., Abiodun, J., Ekpudu, J., Abioro, M. & Akinbola, O. 2020. Assignment problem and its application in Nigerian institutions: Hungarian method approach. International Journal of Applied Operational Research, 10(1), 1–9.
11. Udok, U. V. & Victor-Edema, U. A. 2023. Application of assignment problem in postgraduate course allocation at Ignatius Ajuru University of Education. Faculty of Natural and Applied Sciences Journal of Scientific Innovations, 5(1), 21–33.
12. Wattanasiripong, N. & Sangwaranatee, N. W. 2021. Program for Solving Assignment Problems and Its Application in Lecturer Resources Allocation. Journal of Physics: Conference Series, 2070(1), 012003.
13. Wei, K., Li, J., Ma, C., Ding, M., Chen, C., Jin, S., Han, Z. & Poor, H. V. 2022. Low-Latency Federated Learning Over Wireless. IEEE Journal on Selected Areas in Communications, 40(1), 290–307.
14. Zhang, L., Prorok, A. & Bhattacharya, S. 2021. Pursuer Assignment and Control Strategies in Multi-agent Pursuit-Evasion Under Uncertainties. Frontiers in Robotics and AI, 8, Article 691637.