A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910972817440768 |
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| author | Kirchdorfer, Lukas Özdemir, Konrad Kusenic, Stjepan van der Aa, Han Stuckenschmidt, Heiner |
| author_facet | Kirchdorfer, Lukas Özdemir, Konrad Kusenic, Stjepan van der Aa, Han Stuckenschmidt, Heiner |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation Kirchdorfer, Lukas Özdemir, Konrad Kusenic, Stjepan van der Aa, Han Stuckenschmidt, Heiner Machine Learning 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. |
| title | A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.22381 |