Incorporating Individual Growth into Algorithmic Team Formation

Algorithmic team formation is a well-studied problem in workforce analytics. Most prior research has focused on team-oriented outcomes, such as cost and performance, while individual-oriented outcomes have been limited to operational constraints. We formally define and solve a new variant, the growth-aware team formation problem (GATFP), which forms teams to maximize individual skill growth. Our formulation lets organizations define their own notion of growth and their own policy for allocating growth opportunities, and integrates standard team formation constraints such as covering a portfolio of projects, capping projects per individual, and meeting each project’s skill, level, and coverage requirements. We prove the problem NP-hard and develop a family of metaheuristics: randomized and greedy construction, a ruin-and-recreate large neighborhood search (RR), and a hybrid evolutionary algorithm (HE). Where the objective is linear, a mathematical program provides an exact baseline. We evaluate them under an equal runtime budget on a synthetic benchmark and a real-world-derived dataset. Both metaheuristics surpass the construction baselines and are closely matched, and which one leads depends on the growth model: RR is attractive for its solution quality and implementation simplicity, whereas HE is the stronger choice when an individual’s growth depends on their team’s composition.