Finding optimal mentor-mentee matches: A case study in applied two-sided matching

Christian Haas, Margeret Hall, Sandra L. Vlasnik

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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.

Original languageEnglish (US)
Article numbere00634
Issue number6
StatePublished - Jun 2018


  • Applied mathematics
  • Information science

ASJC Scopus subject areas

  • General


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