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Finding optimal mentor-mentee matches: A case study in applied two-sided matching
Christian Haas1, Margeret Hall1, Sandra L Vlasnik1
1College of Information Science and Technology, University of Nebraska at Omaha, 1110 S 67th Street, Omaha, NE 68182, USA.
Heliyon
|June 27, 2018
Summary
This study applies Two-Sided Matching to pair mentors and mentees in higher education. Evolutionary heuristics and local search effectively identify high-quality matches based on participant preferences.
Area of Science:
- Operations Research
- Higher Education Administration
- Computer Science
Background:
- Two-Sided Matching is a key algorithm for resource allocation based on preferences.
- Existing applications include college admissions and school choice systems.
- This research extends Two-Sided Matching to the higher education mentor-mentee context.
Purpose of the Study:
- To adapt and evaluate Two-Sided Matching algorithms for mentor-mentee pairings.
- To address the complexity of matching with both positive and negative preferences (avoiding certain matches).
- To identify optimal algorithms for generating high-quality mentor-mentee matches.
Main Methods:
- Comparison of various approximation algorithms and heuristics for Two-Sided Matching.
- Evaluation of algorithms' effectiveness in finding desirable matchings.
- Utilized a combination of evolutionary heuristics and local search techniques.
Main Results:
- The general formulation of Two-Sided Matching with avoidance preferences is NP-hard.
- A hybrid approach combining evolutionary heuristics and local search demonstrated superior performance.
- This method successfully generated high-quality mentor-mentee matches close to ideal preferences.
Conclusions:
- The developed matching approach is effective for higher education mentorship programs.
- Combining evolutionary heuristics and local search offers a robust solution for complex matching problems.
- This facilitates improved mentor-mentee relationships through preference-based allocation.
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