An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring

TL;DR

This paper introduces FORLAPS, a novel reinforcement learning framework for context-aware process monitoring that improves resource efficiency and process time, validated across multiple real-world domains.

Contribution

The paper presents FORLAPS, a new adaptive reinforcement learning approach with a process-aware data augmentation technique for prescriptive process monitoring.

Findings

Achieves 31% resource time savings

Reduces process time span by 23%

Outperforms existing models in diverse case studies

Abstract

The application of artificial intelligence and machine learning in business process management has advanced significantly, however, the full potential of these technologies remains largely unexplored, primarily due to challenges related to data quality and availability. We present a novel framework called Fine-Tuned Offline Reinforcement Learning Augmented Process Sequence Optimization (FORLAPS), which aims to identify optimal execution paths in business processes by leveraging reinforcement learning enhanced with a state-dependent reward shaping mechanism, thereby enabling context-sensitive prescriptions. Additionally, to compare FORLAPS with the existing models (Permutation Feature Importance and multi-task Long Short Term Memory model), we experimented to evaluate its effectiveness in terms of resource savings and process time reduction. The experimental results on real-life event logs validate that FORLAPS achieves 31% savings in resource time spent and a 23% reduction in process time span. To further enhance learning, we introduce an innovative process-aware data augmentation technique that selectively increases the average estimated Q-values in sampled batches, enabling automatic fine-tuning of the reinforcement learning model. Robustness was assessed through both prefix-level and trace-level evaluations, using the Damerau-Levenshtein distance as the primary metric. Finally, the model's adaptability across industries was further validated through diverse case studies, including healthcare treatment pathways, financial services workflows, permit applications from regulatory bodies, and operations management. In each domain, the proposed model demonstrated exceptional performance, outperforming existing state-of-the-art approaches in prescriptive decision-making, demonstrating its capability to prescribe optimal next steps and predict the best next activities within a process trace.