A Method for Forming New-Type Construction Project Management Teams Using CSCD-NSGA-II

As intelligent construction technology advances, new projects have become more technology-intensive, collaborative, and multi-objective. Traditional team formation methods based on people’s experience can no longer meet their complex management needs. This study reframes team formation as a multi-objective optimization problem to maximize person–job fit and team collaboration. By introducing a hierarchical penalty mechanism for structured resumes and performing semantic feature extraction on unstructured text via the BERT-base-Chinese model, we develop a job competency model, quantify person–job fit with cosine similarity, and assess team collaboration through MBTI theory and a project-specific scoring framework. An improved algorithm, CSCD-NSGA-II, is proposed, which combines K-means clustering and a modified crowding distance, to maintain solution diversity under constraints. It improves HV by 1.55% and reduces SP by 10.81% compared to the standard NSGA-II. Validation using real projects, simulated data, and algorithm comparisons demonstrates that CSCD-NSGA-II generates teams more efficiently than manual methods. Survey results indicate improved role diversity and the feasibility of collaboration, along with similar task adaptability. The algorithm also outperforms NSGA-II, MOPSO, and SPEA2, supporting intelligent team formation in modern construction.