Hardik Meisheri
Papers
1
Total Citations
30
H-Index
1
About
Hardik Meisheri is a researcher at the intersection of artificial intelligence and operations management, with a primary focus on reinforcement learning (RL) for complex, real-world control systems. His most cited work, "Actor Based Simulation for Closed Loop Control of Supply Chain using Reinforcement Learning" (2019, 30 citations), addresses a critical gap: while RL has revolutionized domains like robotics and gameplay, its application to business-critical systems such as supply chains remains underexplored. Meisheri’s key contribution lies in developing a framework that enables RL agents to be trained in a simulated environment before deployment, ensuring safe and effective closed-loop control of supply chain dynamics. This work is notable for bridging the gap between theoretical RL advances and practical industrial operations, offering a pathway to more resilient and automated logistics. By tackling the challenges of real-world deployment—such as simulation fidelity and agent safety—Meisheri has helped pave the way for AI-driven decision-making in enterprise systems. His research continues to influence how reinforcement learning can be harnessed to optimize complex, stochastic environments, making him a valuable voice in the growing field of AI for operations research.
Research Focus
Key Achievements
Top Papers
- 1