A Simulation-Aided Deep Reinforcement Learning Approach for Optimization of Automated Sorting Center Processes

Operations in a parcel sorting center (SC) are multi-fold which lead to multiple NP-hard optimization problems, namely, parcel-chute assignment, online bin-packing, scheduling, and routing. The advent of multi-agent robotics has accelerated the process of automation in sorting centers which has led to the requirement of sophisticated algorithms to optimize these operations within an SC. To this end, we propose RL - SORT : a simulation-aided deep reinforcement learning based algorithm which jointly optimizes the parcel-chute assignment and online roller-cage (RC) packing problems. Through experimentation on our simulation framework, we show that RL- SORT not only outperforms baselines, but also has a low computational burden. Further, it is able to significantly reduce the number of RCs used, thereby, reducing the transportation costs.