AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation

TL;DR

AgentSimulator introduces a resource-first, agent-based business process simulation method that accurately models resource behaviors and interactions, outperforming traditional control-flow approaches in accuracy and efficiency.

Contribution

This paper presents AgentSimulator, a novel resource-first, agent-based BPS approach that discovers multi-agent systems from event logs for more realistic process simulation.

Findings

Achieves state-of-the-art simulation accuracy.

Lower computation times compared to existing methods.

High interpretability and adaptability to various scenarios.

Abstract

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.