Learning Algorithms for Regenerative Stopping Problems with Applications to Shipping Consolidation in Logistics
This work addresses optimization in logistics, specifically shipping consolidation, but is incremental as it applies existing deep learning methods to a known problem.
The paper tackled regenerative stopping problems by comparing traditional model-based solutions with deep reinforcement learning and imitation learning, demonstrating that deep learning can effectively solve these problems in a real-world shipping consolidation application.
We study regenerative stopping problems in which the system starts anew whenever the controller decides to stop and the long-term average cost is to be minimized. Traditional model-based solutions involve estimating the underlying process from data and computing strategies for the estimated model. In this paper, we compare such solutions to deep reinforcement learning and imitation learning which involve learning a neural network policy from simulations. We evaluate the different approaches on a real-world problem of shipping consolidation in logistics and demonstrate that deep learning can be effectively used to solve such problems.