Port-related emissions are a growing problem for urban areas often located directly next to ports highly frequented by trucks and vessels. Empty truck trips are responsible for a significant share of these emissions. Truck appointment systems (TASs) allow scheduling of truck arrivals and enable collaboration among truckers. Though, TASs leveraging the potential to reduce avoidable emissions due to empty trips have hardly been studied. We aim to show how a TAS following this idea may be designed and evaluate the approach. We thus review requirements for a collaborative TAS and develop a discrete-event simulation model to assess coordinated truck appointments in a practical case of drayage. The results indicate that the approach effectively reduces port-related truck emissions, but might create congestion in the port. The considered case refers to drayage processes, but may also be transferred to the hinterland. The developed simulation model assumes a generic truck appointment process and may also serve to analyze diverse cases.@en

Port-related emissions are a growing problem for urban areas often located directly next to maritime container terminals highly frequented by container trucks. Empty truck trips, caused by a lack of coordination among truckers, are responsible for a significant share of these emissions, and collaboration among truckers is seen as the major opportunity to address this issue. Truck appointment systems (TASs) schedule truck activities and enable collaboration for transportation between terminals and client locations. The aim of this work is to introduce a collaborative planning model to be operated within a TAS and to investigate its impact on emission and cost objectives. Starting with a review of requirements for a TAS with collaboration, an optimization model based on the multiple traveling salesman problem with time windows is developed, leveraging collaboration to reduce costs and emissions. The results for a real-world case demonstrate that the developed approach provides appropriately coordinated truck schedules and effectively reduces truck emissions and costs.@en

Modern ports face significant challenges as strategic nodes of global supply chains, being responsible for the coordination of inbound and outbound flows at deep-sea and in hinterland transport corridors. Digitization and the adoption of disruptive technologies can help ports to tide over operational challenges. Automated Ground Vehicles (AGVs) are an integral part of operations at many modern ports, especially inside container terminals. With the shift to automated transport outside of the terminal areas, these AGVs may form platoons to establish an efficient port hinterland transport corridor. In this work, we propose a new robust optimization approach to assess the time and cost-efficiency of applying such AGV platoons in a container pickup and delivery problem. We develop a bi-objective mixed-integer programming model, which simultaneously minimizes time and cost elements, and also considers emissions. Each transportation task can be carried out by AGVs or conventional trucks, while the number of available vehicles for each mode is uncertain (as they are used to connect different modalities of container transport). The robust optimization model is based on an ellipsoidal uncertainty set to handle this uncertainty and an augmented epsilon constraint method to obtain Pareto-optimal solutions for this multi-objective problem. The developed framework is evaluated in two case studies: the Port of Rotterdam in The Netherlands and the Port of Valparaíso in Chile, with different traveling distances in corridors to a dry port (200 km) and a pre-terminal (11 km), respectively. The results indicate that the new direct delivery scheme by AGV platoons is significantly more cost- and time-efficient than the benchmark and provides a low-carbon emission transportation mode. While the benefits of decreased dwell times (56% on average) and carbon emissions (on average by 10%) are similar for short and long traveling distances, the savings in cost increase (from 4.9% to 8%) with the increased distance in the Rotterdam case.@en