Harshad Khadilkar

Tata Consultancy Services (India), Tennessee Cancer Specialists

Papers

Total Citations

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About

Harshad Khadilkar is a leading researcher at the intersection of artificial intelligence and operations research, with a primary focus on applying Reinforcement Learning (RL) to complex, real-world logistical challenges. His work bridges the gap between theoretical AI and practical industrial automation, particularly in supply chain management and robotic manipulation. Khadilkar’s most influential contribution is his pioneering work on "Actor Based Simulation for Closed Loop Control of Supply Chain using Reinforcement Learning" (2019, 30 citations), which demonstrated how RL can manage business-critical systems—a domain traditionally resistant to such advanced control methods. He has also made significant strides in physical automation, developing a "Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing" (2020, 7 citations) that enables robotic arms to make real-time packing decisions for arbitrary bin sizes. Further pushing the boundaries of computational efficiency, his 2022 work on implementing these algorithms using FPGA hardware (3 citations) showcases a commitment to deploying AI in latency-sensitive environments. Through this body of work, Khadilkar is defining how autonomous systems can learn to optimize the physical flow of goods, from warehouse packing to end-to-end supply chain orchestration.