AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation
This addresses the need for more faithful business process simulation for organizations dealing with decentralized processes, though it is an incremental improvement over existing methods.
The paper tackles the problem of accurately simulating real-world business processes with decentralized decision-making by introducing AgentSimulator, a resource-first approach that models resource behaviors and interactions, achieving state-of-the-art accuracy with significantly lower computation times.
Business process simulation (BPS) is a versatile technique for estimating process performance across various scenarios. Traditionally, BPS approaches employ a control-flow-first perspective by enriching a process model with simulation parameters. Although such approaches can mimic the behavior of centrally orchestrated processes, such as those supported by workflow systems, current control-flow-first approaches cannot faithfully capture the dynamics of real-world processes that involve distinct resource behavior and decentralized decision-making. Recognizing this issue, this paper introduces AgentSimulator, a resource-first BPS approach that discovers a multi-agent system from an event log, modeling distinct resource behaviors and interaction patterns to simulate the underlying process. Our experiments show that AgentSimulator achieves state-of-the-art simulation accuracy with significantly lower computation times than existing approaches while providing high interpretability and adaptability to different types of process-execution scenarios.