用分治法精准建模业务流程到达时间，提升仿真可靠性
A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation
- 将到达时间分解为全局动态、星期差异和日内变化三部分建模
- 在20个真实流程上表现远超传统方法，误差显著降低
- 适合需要高精度流程仿真的企业与研究者
业务流程仿真（BPS）是分析和改进组织流程的关键工具，其核心是案例到达模型，用于确定新案例进入流程的模式。准确的到达建模对仿真结果至关重要，直接影响等待时间和整体周期时长。现有方法多依赖简化的静态到达间隔分布，难以捕捉组织环境中固有的动态与时间复杂性，导致仿真结果不够准确可靠。为此，本文提出自动时间核密度估计（AT-KDE），一种分治策略，综合考虑全局动态、星期差异及日内分布变化，实现高精度与可扩展性的统一。在20个不同流程上的实验表明，AT-KDE在准确性与鲁棒性上均显著优于现有方法，同时保持合理的执行效率。
原文摘要 · Abstract (English)
Business Process Simulation (BPS) is a critical tool for analyzing and improving organizational processes by estimating the impact of process changes. A key component of BPS is the case-arrival model, which determines the pattern of new case entries into a process. Although accurate case-arrival modeling is essential for reliable simulations, as it influences waiting and overall cycle times, existing approaches often rely on oversimplified static distributions of inter-arrival times. These approaches fail to capture the dynamic and temporal complexities inherent in organizational environments, leading to less accurate and reliable outcomes. To address this limitation, we propose Auto Time Kernel Density Estimation (AT-KDE), a divide-and-conquer approach that models arrival times of processes by incorporating global dynamics, day-of-week variations, and intraday distributional changes, ensuring both precision and scalability. Experiments conducted across 20 diverse processes demonstrate that AT-KDE is far more accurate and robust than existing approaches while maintaining sensible execution time efficiency.
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