Integrating Reinforcement Learning into Business Process Mining for Continuous Process Adaptation and Optimization

Abstract

This paper introduces a reinforcement learning (RL) framework for integrating reinforcement learning with business process mining (BPM), aiming to enable continuous adaptation and optimization of business processes in dynamic environments. The proposed approach leverages real-time process data to iteratively enhance process performance by applying RL algorithms that adapt business workflows based on evolving operational conditions. The framework is designed to address the limitations of traditional BPM, which often struggles to adjust in real-time to changing business needs, by enabling autonomous learning and decision-making. The paper explores how RL can be effectively employed to discover process inefficiencies, recommend process modifications, and improve resource allocation by adapting actions based on feedback from previous iterations. Additionally, it presents the integration of RL with process mining techniques, offering a comprehensive model for data-driven decision-making and process improvement. Through case studies and application scenarios, the framework’s potential to enhance operational efficiency, reduce costs, and improve process flexibility is demonstrated. The study concludes by discussing the challenges of applying RL in BPM, such as the need for high-quality data, model interpretability, and scalability, while also identifying future research avenues for the advancement of RL-driven BPM solutions in industry.