| Deep reinforcement learning for dynamic scheduling of a flexible job shop R Liu, R Piplani, C Toro International Journal of Production Research 60 (13), 4049-4069, 2022 | 159 | 2022 |
| A deep multi-agent reinforcement learning approach to solve dynamic job shop scheduling problem R Liu, R Piplani, C Toro Computers & Operations Research 159, 106294, 2023 | 37 | 2023 |
| Can China Achieve its CO2 Emission Mitigation Target in 2030: a System Dynamics Perspective. L Zhang, Z Jiang, R Liu, M Tang, F Wu Polish Journal of Environmental Studies 27 (6), 2018 | 17 | 2018 |
| A review of dynamic scheduling: context, techniques and prospects L Renke, R Piplani, C Toro Implementing Industry 4.0: The Model Factory as the Key Enabler for the …, 2021 | 13 | 2021 |
| Deep reinforcement learning-based dynamic scheduling R Liu Nanyang Technological University, 2022 | 1 | 2022 |
Rajesh PiplaniNanyang Tech. University, SingaporeVerified email at ntu.edu.sg