| Demonstration of reduced airport congestion through pushback rate control I Simaiakis, H Khadilkar, H Balakrishnan, TG Reynolds, RJ Hansman Transportation Research Part A: Policy and Practice 66, 251-267, 2014 | 196 | 2014 |
| Estimation of aircraft taxi fuel burn using flight data recorder archives H Khadilkar, H Balakrishnan Transportation Research Part D: Transport and Environment 17 (7), 532-537, 2012 | 183 | 2012 |
| A scalable reinforcement learning algorithm for scheduling railway lines H Khadilkar IEEE Transactions on Intelligent Transportation Systems 20 (2), 727-736, 2018 | 120 | 2018 |
| Optimising lockdown policies for epidemic control using reinforcement learning: An AI-driven control approach compatible with existing disease and network models H Khadilkar, T Ganu, DP Seetharam Transactions of the Indian National Academy of Engineering 5 (2), 129-132, 2020 | 68 | 2020 |
| Network congestion control of airport surface operations H Khadilkar, H Balakrishnan Journal of Guidance, Control, and Dynamics 37 (3), 933-940, 2014 | 65 | 2014 |
| Data-enabled stochastic modeling for evaluating schedule robustness of railway networks H Khadilkar Transportation Science 51 (4), 1161-1176, 2017 | 57 | 2017 |
| Multi-user energy consumption monitoring and anomaly detection with partial context information P Arjunan, HD Khadilkar, T Ganu, ZM Charbiwala, A Singh, P Singh Proceedings of the 2nd ACM international conference on embedded systems for …, 2015 | 53 | 2015 |
| High confidence networked control for next generation air transportation systems P Park, H Khadilkar, H Balakrishnan, CJ Tomlin IEEE Transactions on Automatic Control 59 (12), 3357-3372, 2014 | 50 | 2014 |
| A generalized reinforcement learning algorithm for online 3d bin-packing R Verma, A Singhal, H Khadilkar, A Basumatary, S Nayak, HV Singh, ... arXiv preprint arXiv:2007.00463, 2020 | 43 | 2020 |
| Actor based simulation for closed loop control of supply chain using reinforcement learning S Barat, H Khadilkar, H Meisheri, V Kulkarni, V Baniwal, P Kumar, ... Proceedings of the 18th international conference on autonomous agents and …, 2019 | 36 | 2019 |
| Individual and aggregate electrical load forecasting: One for all and all for one S Bandyopadhyay, T Ganu, H Khadilkar, V Arya Proceedings of the 2015 ACM Sixth International Conference on Future Energy …, 2015 | 33 | 2015 |
| Reinforcement learning for multi-product multi-node inventory management in supply chains NN Sultana, H Meisheri, V Baniwal, S Nath, B Ravindran, H Khadilkar arXiv preprint arXiv:2006.04037, 2020 | 31 | 2020 |
| Scalable multi-product inventory control with lead time constraints using reinforcement learning H Meisheri, NN Sultana, M Baranwal, V Baniwal, S Nath, S Verma, ... Neural Computing and Applications 34 (3), 1735-1757, 2022 | 28 | 2022 |
| Integrated control of airport and terminal airspace operations H Khadilkar, H Balakrishnan IEEE Transactions on Control Systems Technology 24 (1), 216-225, 2015 | 26 | 2015 |
| Revisiting state augmentation methods for reinforcement learning with stochastic delays S Nath, M Baranwal, H Khadilkar Proceedings of the 30th ACM International Conference on Information …, 2021 | 21 | 2021 |
| A multi-modal unscented Kalman filter for inference of aircraft position and taxi mode from surface surveillance data H Khadilkar, H Balakrishnan 11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference …, 2011 | 18 | 2011 |
| A rolling horizon optimisation model for consolidated hump yard operational planning S Raut, SK Sinha, H Khadilkar, S Salsingikar Journal of Rail Transport Planning & Management 9, 3-21, 2019 | 17 | 2019 |
| A reinforcement learning framework for container selection and ship load sequencing in ports R Verma, S Saikia, H Khadilkar, P Agarwal, G Shroff, A Srinivasan Proceedings of the 18th International Conference on Autonomous Agents and …, 2019 | 16 | 2019 |
| Analysis and modeling of airport surface operations HHD Khadilkar Massachusetts Institute of Technology, 2011 | 16 | 2011 |
| Fast approximate solutions using reinforcement learning for dynamic capacitated vehicle routing with time windows NN Sultana, V Baniwal, A Basumatary, P Mittal, S Ghosh, H Khadilkar arXiv preprint arXiv:2102.12088, 2021 | 15 | 2021 |
Tanuja GanuMicrosoft ResearchVerified email at microsoft.com
