A set partitioning based goal programming model for the team formation problem

• Published in: International transactions in operational research 2022-01, Vol.29 (1), p.301-322
• Main Authors: Daş, Gülesin Sena, Altınkaynak, Büşra, Göçken, Tolunay, Türker, Ahmet Kürşad
• Format: Article
• Language: English
• Subjects: Algorithms; Chemical partition; Datasets; Goal programming; Heuristic methods; Model testing; multi‐objective optimization; Operations research; Partitioning; social network; Social networks; team formation; Teams
• ISSN: 0969-6016; 1475-3995
• DOI: 10.1111/itor.13022

In this paper, we offer a multi‐objective set‐partitioning formulation for team formation problems using goal programming. Instead of selecting team members to teams, we select suitable teams from a set of teams. This set is generated using a heuristic algorithm that uses the social network of potential team members. We then utilize the proposed multi‐objective formulation to select the desired number of teams from this set that meets the skill requirements. Therefore, we ensure that selected teams include individuals with the required skills and effective communication with each other. Two real datasets are used to test the model. The results obtained with the proposed solution are compared with two well‐known approaches: weighted and lexicographic goal programming. Results reveal that weighted and lexicographic goal programming approaches generate almost identical solutions for the datasets tested. Our approach, on the other hand, mostly picks teams with lower communication costs. Even in some cases, better solutions are obtained with the proposed approach. Findings show that the developed solution approach is a promising approach to handle team formation problems.