Sahil Belsare, MS'21, Industrial Engineering, received the Best Master's Thesis Award from the EuroSim Conference in Amsterdam, Netherlands.
Thesis Title
"A Deep Reinforcement Learning Approach for an Adaptive Job Shop Scheduling"
Belsare used a deep reinforcement learning approach to solve a stochastic flexible job scheduling problem, which was innovative at that time. His research demonstrated how modern AI techniques could be applied to complex manufacturing scheduling challenges.
The thesis delves into defining a reward function that trains an RL agent to effectively capture queue characteristics and dynamically adjust job scheduling. This work represents a significant contribution to the intersection of simulation and reinforcement learning.
Sahil was advised by MIE Associate Teaching Professor Mohammad Dehghani during his graduate studies at Northeastern University.
Sahil currently works as a Simulation-Optimization Engineer at Walmart Innovation Engineering, where he continues to apply his expertise in simulation and reinforcement learning to real-world problems.