Resource allocation plays a critical role in minimizing cycle time and improving the efficiency of business processes. Recently, Deep Reinforcement Learning (DRL) has emerged as a powerful tool to optimize resource allocation policies in business processes. In the DRL framework, an agent learns a policy through interaction with the environment, guided solely by reward signals that indicate the quality of its decisions. However, existing algorithms are not suitable for dynamic environments such as business processes. Furthermore, existing DRL-based methods rely on engineered reward functions that approximate the desired objective, but a misalignment between reward and objective can lead to undesired decisions or suboptimal policies. To address these issues, we propose a rollout-based DRL algorithm and a reward function to optimize the objective directly. Our algorithm iteratively improves the policy by evaluating execution trajectories following different actions. Our reward function directly decomposes the objective function of minimizing the mean cycle time. Maximizing our reward function guarantees that the objective function is minimized without requiring extensive reward engineering. The results show that our method consistently learns the optimal policy in all six evaluated business processes, outperforming the state-of-the-art algorithm that can only learn the optimal policy in two of the evaluated processes.

本研究解决了当前深度强化学习（DRL）在动态商业环境中资源分配算法的适用性问题。提出了一种创新的基于滚动的DRL算法和直接优化目标的奖励函数，通过评估不同操作的执行路径来迭代改进策略。实验结果表明，该方法在六个业务流程中始终学习到最佳策略，显著优于仅能在两个流程中学习到最佳策略的现有算法。

基于滚动的算法和奖励函数在业务流程中高效资源分配的研究