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

2025-05-28

AI Summary

Existing business process simulation (BPS) predominantly models case arrivals using static probability distributions, failing to capture multi-scale temporal dynamics inherent in organizational contexts—leading to simulation inaccuracies. To address this, we propose a divide-and-conquer approach for dynamic arrival time modeling, the first to systematically integrate global trends, weekly seasonality, and intra-day nonstationarity. Our core contribution is the Auto Time Kernel Density Estimation (AT-KDE) framework, which unifies multi-granularity temporal decomposition, dynamic bandwidth adaptation, and nonstationary kernel density estimation. Evaluated on 20 real-world business processes, AT-KDE significantly improves arrival time prediction accuracy, reduces simulation errors in waiting time and cycle time by up to 37%, and maintains high computational efficiency and scalability. The framework delivers a more reliable, interpretable, and temporally grounded paradigm for dynamic arrival modeling in BPS.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Time-Series/Data Streams

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Modeling dynamic case-arrival times in business processes
• Overcoming oversimplified static inter-arrival time distributions
• Capturing temporal complexities for accurate simulation outcomes

Innovation

• Divide-and-conquer approach for arrival modeling
• Auto Time Kernel Density Estimation (AT-KDE)
• Captures global, weekly, and intraday variations