Vinita Baniwal

Papers

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About

Vinita Baniwal’s research lies at the intersection of artificial intelligence and operations management, with a primary focus on applying reinforcement learning (RL) to complex, real-world control systems. Her 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 transformed fields like robotics and gameplay, its application to business-critical operations such as supply chain management remains underexplored. Baniwal’s contribution is a novel actor-based simulation framework that enables RL agents to be trained in a closed-loop environment before deployment, ensuring robust and adaptive decision-making under uncertainty. This work not only demonstrates the feasibility of RL for supply chain control but also provides a scalable methodology for training intelligent agents in high-stakes industrial settings. By bridging the divide between cutting-edge AI techniques and practical operational challenges, Baniwal has laid foundational groundwork for autonomous, self-optimizing supply chains. Her research is particularly valuable for students and practitioners seeking to harness RL beyond traditional benchmarks, offering a clear pathway from simulation to real-world impact. With growing interest in AI-driven automation, her work continues to inspire further exploration into intelligent control for logistics and manufacturing systems.