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Stochastic Dynamic Intermodal Transportation with Eco-labels
Eco-labels are a way to benchmark transportation shipments with respect to their environmental impact. In contrast to an eco-labeling of consumer products, emissions in transportation depend on several operational factors like the mode of transportation (e.g., train or truck) or a vehicle’s current and potential future capacity utilization when new orders are added for consolidation. Thus, satisfying eco-labels and doing this cost-efficiently is a challenging task when dynamically routing orders in an intermodal network. In this project, we analyze how reinforcement learning techniques can be adapted to our problem and show their advantages and the impact of Eco-labels in a comprehensive study for intermodal transport via train and trucks in Europe.

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