A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation

TL;DR

This paper introduces AT-KDE, a divide-and-conquer method for more accurately modeling process arrival times in business process simulation by capturing temporal dynamics and variations.

Contribution

It presents a novel AT-KDE approach that improves accuracy and scalability in modeling arrival times by incorporating temporal variations and global dynamics.

Findings

AT-KDE outperforms existing methods in accuracy and robustness.

It maintains efficiency suitable for large-scale processes.

Experiments on 20 processes validate its effectiveness.

Abstract

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.