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.

中文翻译：

---

大型组织中的团队组建：深度强化学习方法


高效的团队组建对于人力资源管理至关重要，尤其是在大型企业组织继续扁平化并越来越多地由项目驱动的情况下。有效调度内部部门和降低员工调度成本是基本目标。本白皮书解决了从现有网络中提取具有满足项目要求的必要技能的员工的挑战，同时最大限度地减少对原始部门网络的干扰。为了解决这个问题，我们将组织建模为一个图形，其中每个员工都是一个节点，边缘代表他们之间的通信。我们将团队组建表述为图上的组合优化问题。我们首先创新性地设计了员工替代和组织措施，以改变图表上的结构。为了克服在庞大的组织结构和资源限制下团队组建的复杂性，我们提出了图组合优化 DQN 框架。这种新颖的方法结合了强化学习和图神经网络。通过利用图神经网络，我们根据员工的基本信息、技能和与其他员工的沟通模式来了解员工的表现。此外，在测试过程中，我们使智能体能够通过学习不断改进其解决方案，并避免优化早期决策的陷阱，这可能会阻碍后期决策的修改。这是通过逐步构建解决方案的子集来实现的。通过将 GCO-DQN 与五种最先进的方法进行比较，我们使用真实世界的企业数据集和合成数据集来证明 GCO-DQN 框架的优越性。