MODIFIED HUNGARIAN METHOD FOR LECTURER-TO-COURSE ASSIGNMENT: A MULTI-OBJECTIVE MATHEMATICAL PROGRAMMING MODEL FOR OPTIMIZING PREFERENCES AND COMPETENCY (PC MO-MHM)
DOI:
https://doi.org/10.24191/mjoc.v10i2.5562Keywords:
Competency, Lecturers-to-Courses Assignment, Mathematical Programming, Modified Hungarian Method, PreferencesAbstract
Efficient lecturer-to-course assignment is crucial for ensuring both faculty satisfaction and optimal teaching outcomes in higher education institutions. This study presents an advanced optimization model based on the Modified Hungarian Method (MHM) to address this challenge by integrating lecturers' preference levels and competency scores. While previous research has primarily focused on the traditional Hungarian Method (HM), limited attention has been given to its modified version. Moreover, the incorporation of preference-competency-based criteria in lecturer assignments is still lacking. To bridge these gaps, this study develops a mathematical programming approach to refine the MHM framework. The proposed model, called the Preference-Competency Multi-Objective MHM (PC MO-MHM), aims to achieve two key objectives: maximizing lecturers’ preferences and maximizing lecturers’ competencies. Competency is assessed across three elements: knowledge, skills, and teaching motivation. Data were gathered through an online survey involving Mathematics lecturers teaching undergraduate courses at the public university in Malaysia. By utilizing the collected data on preference levels and competency scores, the PC MO-MHM model was implemented using MATLAB’s intlinprog function to generate an optimized lecturer-to-course assignment plan, limiting each lecturer to a maximum of three courses. The findings highlight that the PC MO-MHM model effectively determines the most suitable course assignments based on lecturers’ preferences and competencies. The enhanced MHM framework provides a practical tool for optimizing course-teaching assignment planning. The model potentially not only improves teaching quality but also minimizes mismatches between lecturers and courses, fostering better academic outcomes and increased faculty satisfaction. Ultimately, this study contributes towards refining lecturers’ assignment processes, paving the way for more effective and efficient resource management in academia.
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