Monika Gajrani
Papers
1
Total Citations
30
H-Index
1
About
Monika Gajrani’s research lies at the intersection of artificial intelligence, reinforcement learning, and supply chain management. Her most-cited work, “Actor Based Simulation for Closed Loop Control of Supply Chain using Reinforcement Learning” (2019, 30 citations), addresses a critical gap in applying RL to business-critical systems. While RL has transformed domains like robotics and gameplay, Gajrani’s pioneering contribution demonstrates how to train agents in simulated environments before real-world deployment, enabling closed-loop control of complex supply chains. This work bridges the gap between theoretical RL advances and practical operational management, offering a framework for more adaptive, intelligent logistics. By showing that RL can manage the inherent uncertainty and dynamism of supply chains, Gajrani has opened new pathways for automation in industry. Her research is particularly valuable for students and practitioners seeking to apply cutting-edge AI to real-world operational challenges, proving that reinforcement learning is not just for games and robots—it can revolutionize how businesses manage their most critical systems.
Research Focus
Key Achievements
Top Papers
- 1