AI-Based Team Allocation Optimization Using the Hungarian Algorithm in Industrial Projects
Abstract
Efficient team allocation is critical for the success of industrial projects, where multiple tasks must be assigned to employees with diverse skills under time and resource constraints. Optimal task-team mapping ensures balanced workloads, enhances productivity, and minimizes delays, making it a fundamental challenge in project management and operations research. Typical methods of allocation rely on manual decision-making or heuristic methods while ignoring factors like skill-task fit, project priorities, workflow, or project optimization actions. Therefore, manual or heuristic team allocation methods can cause inefficient team resource utilization and skill-task mismatches leading to project inefficiency in larger-scale industrial contexts e.g. construction, shipbuilding, manufacturing, etc. To solve this problem, we propose AI-Based Team Allocation Optimization using Hungarian Algorithm (ATAO-HA) framework which combines artificial intelligence for skill based assessments and task requirement modeling with the Hungarian Algorithm for optimal task-team assignment. The ATAO-HA algorithm efficiently computes a minimum-cost allocation matrix to optimally allocate task allocation while considering factors of deemed skill suitability of the workforce, workload balance, and project deadlines. Experiments conducted on simulated industrial project datasets have showed that ATAO-HA can significantly improve allocation efficiency over heuristic and greedy methods. The algorithm reduced task mismatches by 28%, improved balance of workload distribution by 34%, and improved overall project completion time. Furthermore, the ATAO-HA algorithm was demonstrated to have reasonable scalability for larger project datasets containing hundreds of tasks and team members. Finally, ATAO-HA provides industrial organizations with a systematic, interpretable, computationally efficient framework for team allocation in industrial projects. By matching team member skills to project task needs, these organizations can, potentially improve organizational efficiency, decrease project risk, and promote data-driven-methodologies for allocation based decision making in project management.