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

Business Process Simulation (BPS) is a critical tool for ana-lyzing and improving organizational processes by estimating the impactof process changes. A key component of BPS is the case-arrival model,which determines the pattern of new case entries into a process. Althoughaccurate case-arrival modeling is essential for reliable simulations—asit influences waiting and overall cycle times—existing approaches oftenrely on oversimplified static distributions of inter-arrival times. Theseapproaches fail to capture the dynamic and temporal complexities in-herent in organizational environments, leading to less accurate and reli-able outcomes. To address this limitation, we propose Auto Time Ker-nel Density Estimation (AT-KDE), a divide-and-conquer approach thatmodels arrival times of processes by incorporating global dynamics, day-of-week variations, and intraday distributional changes, ensuring bothprecision and scalability. Experiments conducted across 20 diverse pro-cesses demonstrate that AT-KDE is far more accurate and robust thanexisting approaches while maintaining sensible execution time efficiency.