Marshall, Sarah EMohaghegh, Mahsa2025-02-182025-02-182025-02Computers & Operations Research, ISSN: 0305-0548 (Print), Elsevier BV, 107002-107002. doi: 10.1016/j.cor.2025.1070020305-0548http://hdl.handle.net/10292/18687Women remain significantly underrepresented in the fields of science, technology, engineering and mathematics (STEM), but positive mentoring relationships can help mitigate the challenges they face when studying and working in these areas. To support female university students in STEM, the Auckland University of Technology (AUT) established the Women in Tech mentorship programme in 2019. Initially, the matching of mentees and mentors was achieved manually, but as the programme’s popularity grew, this process became increasingly time consuming. This study addresses the challenges associated with assigning mentees to mentors by automating the matching process based on mentee and mentor attributes. A rule-based heuristic is proposed and compared with a linear programming (LP) approach. Numerical experiments were conducted to evaluate the performance of these algorithms across various scenarios. The rule-based heuristic provides a simple and easily understandable way to allocate mentees and mentors that performs nearly as well as an optimal matching provided by the LP approach. Applying these algorithms to real data from the AUT Women in Tech mentorship programme, it was found that they outperformed manual matching in several performance metrics.© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/4901 Applied Mathematics3509 Transportation, Logistics and Supply Chains49 Mathematical Sciences35 Commerce, Management, Tourism and ServicesWomen's HealthGeneric health relevance0102 Applied Mathematics0103 Numerical and Computational MathematicsOperations Research3509 Transportation, logistics and supply chains4901 Applied mathematicsJournal ArticleOpenAccess10.1016/j.cor.2025.107002