Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

M Khadivi, T Charter, M Yaghoubi, M Jalayer… - Computers & Industrial …, 2025 - Elsevier
Abstract Machine scheduling aims to optimally assign jobs to a single or a group of
machines while meeting manufacturing rules as well as job specifications. Optimizing the …

FORLAPS: An Innovative Data-Driven Reinforcement Learning Approach for Prescriptive Process Monitoring

M Abbasi, M Khadivi, M Ahang, P Lasserre… - arxiv preprint arxiv …, 2025 - arxiv.org
We present a novel 5-step framework called Fine-Tuned Offline Reinforcement Learning
Augmented Process Sequence Optimization (FORLAPS), which aims to identify optimal …

An Innovative Next Activity Prediction Approach Using Process Entropy and DAW-Transformer

H Zare, M Abbasi, M Ahang, H Najjaran - arxiv preprint arxiv:2502.10573, 2025 - arxiv.org
Purpose-In Business Process Management (BPM), accurate prediction of the next activities
is vital for operational efficiency and decision-making. Current Artificial Intelligence …

An Innovative Data-Driven Reinforcement Learning Approach for Prescriptive Process Monitoring

M Abbasi, M Khadivi, M Ahang, P Lasserre… - Available at SSRN … - papers.ssrn.com
The application of artificial intelligence (AI) and machine learning (ML) in business process
management has progressed significantly; however, the full potential of these technologies …

[PDF][PDF] Redefining Business Process Management with AI: Intelligent Workflows for the Digital Age.

SO Razaq - researchgate.net
This research article aims to identify the new perspectives that occurred in the Business
Process Management (BPM) during the digital era, mainly the introduction of the Artificial …