Team formation in large organizations: A deep reinforcement learning approach

Abstract

Efficient team formation is critical to human resource management, particularly as large enterprise organizations continue to flatten and are increasingly driven by projects. Efficiently scheduling internal departments and reducing employee scheduling costs are essential objectives. This paper addresses the challenge of extracting employees from the existing network who possess the necessary skills to meet project requirements while minimizing the disruption to the original department network. To tackle this problem, we model the organization as a graph, where each employee is a node, and edges represent communication between them. We formulate team formation as a combinatorial optimization problem on the graph. We first innovatively design the employee replacement and organizational measures for changing structures on the graph. To overcome the complexity of team formation under vast organizational structures and resource constraints, we propose the Graph Combinatorial Optimization DQN framework. This novel approach combines reinforcement learning and graph neural networks. By leveraging graph neural networks, we learn employee representations based on their basic information, skills, and communication patterns with other employees. Furthermore, during testing, we enable the agent to continuously improve its solutions through learning and avoid the pitfall of optimizing early decisions that may hinder the modification of later decisions. This is achieved by incrementally building subsets of solutions. We demonstrate the superiority of the GCO-DQN framework using both the real-world enterprise dataset and a synthetic dataset by comparing GCO-DQN with five state-of-the-art methods.