Synchromodal freight transport re-planning under service time uncertainty: An online model-assisted reinforcement learning

The objective of this study is to address the issue of service time uncertainty in synchromodal freight transport, which can cause delays, inefficiencies, and reduced satisfaction for shippers. The proposed solution is an online deep Reinforcement Learning (RL) approach that takes into account the service time uncertainty, assisted by an...

Global synchromodal shipment matching problem with dynamic and stochastic travel times: a reinforcement learning approach

Global synchromodal transportation involves the movement of container shipments between inland terminals located in different continents using ships, barges, trains, trucks, or any combination among them through integrated planning at a network level. One of the challenges faced by global operators is the matching of accepted shipments with...