Two-Sided Matching is a well-established approach to find allocations and matchings based on the participants' preferences. While its most prominent applications are College Admissions and School Choice problems, this paper applies the concept to the matching of mentors to mentees in a higher education context. Both mentors and mentees have preferences with whom they ideally want to be matched, as well as who they want to avoid. As the general formulation for these types of preferences is NP-hard, several existing approximation algorithms and heuristics are compared with respect to their ability to find a matching with desirable properties. The results show that a combination of evolutionary heuristics and local search approaches works best in finding high-quality solutions, allowing us to find mentor-mentee pairs which are close to the respective ideal match.
Österreichische Systematik der Wissenschaftszweige (ÖFOS)
- 102001 Artificial Intelligence