Efficient resource allocation is a key challenge in business process management, with direct implications for cost, throughput time, and utilization. While recent Reinforcement Learning (RL) approaches have shown promise in deriving adaptive allocation policies, they typically neglect inter-resource collaboration patterns that can strongly influence real-world task handovers. Recognizing this, this paper introduces the first approach for multi-objective optimization of resource-level decision-making, enabling the recommendation of person-specific handover policies. To achieve this, our work combines an existing Multi-Agent System-based process simulator with a multi-objective evolutionary algorithm. The resulting approach produces Pareto-optimal, resource-specific policies that optimize the process across multiple objectives. Experimental results on synthetic and real-world datasets show that our approach reduces costs by an average of 37% and waiting time by 58%, consistently outperforming heuristic baselines and demonstrating the potential of leveraging collaboration-aware optimization to improve process performance.

翻译：高效资源分配是业务流程管理中的关键挑战，直接影响成本、处理时间和资源利用率。尽管近期基于强化学习的方法在推导自适应分配策略方面展现出潜力，但它们通常忽略了现实中影响任务交接的跨资源协作模式。基于此，本文首次提出了一种资源级决策的多目标优化方法，能够推荐人员特定的交接策略。为实现这一目标，我们的工作将现有的基于多智能体系统的流程模拟器与多目标进化算法相结合。所提出的方法可生成帕累托最优的资源特定策略，从多个目标维度优化流程。在合成数据集和真实数据集上的实验结果表明，我们的方法平均降低37%的成本和58%的等待时间，持续优于启发式基线方法，证明了利用协作感知优化提升流程性能的潜力。