2024-03-212024-03-212019Çavdur F. vd. (2019). "A group-decision making and goal programming-based solution approach for the student-project team formation problem". Journal of the Faculty of Engineering and Architecture of Gazi University, 34(1), 505-521.1300-18841304-4915https://dergipark.org.tr/tr/download/article-file/680128https://hdl.handle.net/11452/40555In this study, an Analytic Hierarchy Process (AHP) and goal programming-based solution approach for the student-project team formation problem is proposed. In the first and second phases of a two-phase goal programming-based approach developed in a recent study of authors, students and advisers are allocated to project teams considering several criteria. The AHP method is used to determine the criteria weights for the goal programming models. Surveys are conducted with students and advisors to determine the criteria weights for each student and advisor. Since the criteria weights are different for different decision makers, a distance-based non-linear (DBNL) mathematical programming model is used to determine a group decision. The proposed approach is implemented on a real-life project-team formation problem. The results are compared with the real-life allocations in terms of the problem criteria considered in the study and it is observed that our approach produces significantly more satisfactory results. Additionally, although it takes a considerable amount of time to perform the allocations in real-life, using the proposed approach, allocations can be performed in a few hours, including the only once-conducted survey time. The presented approach is proposed for a special problem in this study; however, it can be easily adapted to other project-team formation problems.eninfo:eu-repo/semantics/openAccessStudent-project team formationMulti-criteria decision makingGroup-decision makingAnalytic hierarchy processGoal programmingAHPAssignmentAllocationPreferencesweıghtsModelVikorEngineeringElectrical engineering, electronics & computer scienceArtificial intelligence & machine learningFuzzy setsArticle0004624906000362-s2.0-85064332933505521341https://doi.org/10.17341/gazimmfd.416511Engineering, MultidisciplinaryTeam Formation; Genetic Algorithm; NP-complete Problem