In this paper, we will study a typical problem in inland container shipping, concerning the barge transportation of maritime containers between a dry port and a set of seaport terminals. The barges depart from the dry port and visit a set of sea terminals, where containers need either to be dropped off or picked up. The goal is to achieve economies of scale with barges and avoid trucking as much as possible. The decision thus involves finding the best allocation of containers to barges in order to guarantee on-time delivery and meet capacity restrictions. The problem will be modeled as a variant of the split vehicle routing problem with simultaneous pickups and deliveries coupled with time features. The model includes parameters that can be tuned to improve barge utilization and travelling distance. A hybrid local search meta-heuristic algorithm, combined with a branch-and-cut solver, will be developed to solve the model. Numerical experiments have been conducted to test the performance of the algorithm and provide solution analysis for practical insights. Real-world data has been collected from a local barge operator based in the Port of Rotterdam region and will be used as input for the experiments. This will result in an in-depth analysis into current planning practices. The proposed framework complements existing models in the literature and contributes to the development of a comprehensive set of decision support tools, which help in the decision-making process for inland terminals.

@en

In this work, we develop a mixed integer linear optimization model that can be used to select appropriate sources, capture technologies, transportation network and CO₂ storage sites and optimize for a minimum overall cost for a nationwide CO₂ emission reduction in the Netherlands. Five different scenarios are formulated by varying the location of source and storage sites available in the Netherlands. The results show that the minimum overall cost of all scenarios is €47.8 billion for 25 years of operation and 54Mtpa capture of CO₂. Based on the investigated technologies, this work identifies Pressure Swing Adsorption (PSA) as the most efficient for post-combustion CO₂ capture in the Netherlands. The foremost outcome of this study is that the capture and compression is the dominant force contributing to a majority of the cost.

@en

A major challenge for the industrial deployment of a CO₂ emission reduction methodology is to reduce the overall cost and the integration of all the nodes in the supply chain for CO₂ emission reduction. In this work, we develop a mixed integer linear optimization model that selects appropriate sources, capture process, transportation network and CO₂ storage sites and optimize for a minimum overall cost. Initially, we screen the sources and storage options available in the Netherlands at different levels of detail (locations and industrial activities) and present the network of major sources and storage sites at the more detailed level. Results for a case study estimate the overall optimized cost to be €47.8 billion for 25 years of operation and 54 Mtpa reduction of CO₂ emissions (30% of the 2013 levels). This work also identifies the preferred technologies for the CO₂ capture and we discuss the reasons behind it. The foremost outcome of this case study is that capture and compression consumes the majority of the costs and that further optimization or introduction of new efficient technologies for capture can cause a major reduction in the overall costs.

@en

In this paper, we present a DSS that generates schedules for the transportation of containers by barge in the hinterland, in particular from sea terminals to an inland terminal. As a case study, we propose the transportation from the ports of Rotterdam and Antwerp to a terminal in the south of the Netherlands, where the problem is typical. This problem is modeled as a heterogeneous fleet vehicle routing problem. The main decision is based on the trade-off of either consolidating containers to generate economies of scale with barges or alternatively dispatch, expensively and quickly, single containers by truck. The DSS is flexible as it can be applied to different settings by properly tuning the several parameters in the model. With numerical experiments, based on real world data, we evaluate the effectiveness of this system and its applicability.@en

The increase in traded container volumes worldwide puts pressure on the hinterland road network, leading congestion and emission problems. This leads to a requirement to develop intermodal transportation systems. In this chapter, we analyze the most important features of such container transportation systems for the hinterland supply chain. At the network design level, we review the current state of the art and we identify avenues for future research. Among others, we highlight that the coordination of container shipments across the container supply chain is a particularly relevant issue as hinterland networks involve several actors. At the operational level, we characterize the most important factors influencing the trade-off between intermodal transportation and truck-only deliveries. In addition, we provide a case study of coordination at an intermodal barge terminal in the Netherlands. We highlight that the exchange of information is the key enabler for efficient hinterland intermodal transportation and we show that a better information system can be of crucial importance.@en