Resource allocation is the assignment of resources to activities that must be executed in a business process at a particular moment at run-time. While resource allocation is well-studied in other fields, such as manufacturing, there exist only a few methods in business process management. Existing methods are not suited for application in large business processes or focus on optimizing resource allocation for a single case rather than for all cases combined. To fill this gap, this paper proposes two learning-based methods for resource allocation in business processes: a deep reinforcement learning-based approach and a score-based value function approximation approach. The two methods are compared against existing heuristics in a set of scenarios that represent typical business process structures and on a complete network that represents a realistic business process. The results show that our learning-based methods outperform or are competitive with common heuristics in most scenarios and outperform heuristics in the complete network.

翻译：资源分配是指在运行时将资源分配给业务流程中特定时刻必须执行的活动。尽管资源分配在制造等其他领域已有深入研究，但在业务流程管理中仅有少数方法可用。现有方法不适用于大型业务流程的部署，或仅针对单个案例而非所有案例的联合优化。为填补此空白，本文提出两种基于学习的业务流程资源分配方法：基于深度强化学习的方法和基于分数的价值函数近似方法。我们将这两种方法与代表典型业务流程结构的一组场景中的现有启发式算法进行比较，并在代表真实业务流程的完整网络中进行对比。结果表明，我们的基于学习方法在大多数场景中优于或与常见启发式算法具有竞争力，且在完整网络中优于启发式算法。