Stable Matching Algorithm Approach to Resolving Institutional Projects Allocation and Distribution Optimization Problems

Abstract

The manual approach to resolving assignment issues involving the distribution or assignment of graduate students to lecturers for projects or thesis studies continues to be difficult and time-consuming for both the involved departments and the students. Even though it is evident that a student must be paired with a lecturer, there are significant problems with matching due to individual preferences, specializations, interests, prior student-teacher relationships, and other factors directly or indirectly related to the distribution process. This study uses a reliable matching technique to address issues with project allocation and distribution optimization problems, ten lecturers and ten graduate students participated. A matching algorithm is provided randomly, with students favouring a lecturer (although these roles are reversible). The lists of preferences for students and lecturers are input into the algorithm. Students and lecturers are split into two categories throughout the algorithm: those previously selected and those who have not yet been determined (free). Both instructors and students are initially accessible. The method chooses one student X randomly from the group of free students, provided that the group is not empty. Student X prefers a lecturer (let's say lecturer Y) who he rates among the top lecturers he has never previously picked. The Python programming language created and executed the stable matching algorithm. The stable matching algorithm makes the allocation faster, more accurate and timely compared to manual methods.