Maryam Ahang

About

Dr. Maryam Ahang is a leading researcher at the intersection of artificial intelligence and operations research, with a primary focus on applying deep reinforcement learning (DRL) to complex machine scheduling problems. Her most influential work, "Deep Reinforcement Learning for Machine Scheduling: Methodology, the State-of-the-Art, and Future Directions" (2025), has already garnered 30 citations, establishing her as a key voice in this rapidly evolving field. Dr. Ahang's contributions are particularly notable for providing a comprehensive methodological framework that bridges theoretical DRL advances with practical scheduling challenges in manufacturing and computing systems. Her earlier 2023 paper on the same topic (15 citations) laid crucial groundwork by systematically categorizing DRL approaches for scheduling, identifying critical research gaps, and proposing future directions that have since shaped numerous follow-up studies. Through her work, Dr. Ahang has demonstrated how intelligent agents can learn optimal scheduling policies directly from experience, moving beyond traditional heuristic methods. Her research holds significant promise for revolutionizing production planning, resource allocation, and real-time decision-making in smart factories and cloud computing environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

   30 citations · 2025
2. 2
   Deep Reinforcement Learning for Machine Scheduling: Methodology, the State-of-The-Art, and Future Directions

   15 citations · 2023

Key Collaborators

Contact & Links