Fair Team Recommendations for Multidisciplinary Projects

Abstract

The focus of this work is on the problem of team recommendations, in which teams have multidisciplinary requirements and team members’ selection is based on the match of their skills and the requirements. When assembling multiple teams there is also a challenge of allocating the best members in a fair way between the teams. We formally define the problem and propose a brute force and a faster heuristic method as solutions to create team recommendations to multidisciplinary projects. Furthermore, to increase the fairness between the recommended teams, the K-rounds and Pairs-rounds methods are proposed as variations of the heuristic approach. Several different test scenarios are executed to analyze and compare the effectiveness of these methods.