We propose user-centric booking platforms for end-to-end freight transport as a requirement for the scaling of synchromodal transport and a new avenue for transport and logistics research. We start with the assertion that synchromodal transport is still an unapplied concept due to the strong heterogeneity and disconnection of the transport offer and the huge variety of cargo requests. We suggest that open digital platforms with a focus on shippers can help create transparency that benefits shippers and carriers, and may increase the efficiency in the use of network capacity. We denote the concept Freight Mobility as a Service (FMaaS). Current digital platforms predominantly operate under the assumption that transport services are on-demand, often with flexible lead times, overlooking the structured nature of most actual transport operations. FMaaS challenges this paradigm by recognizing that a significant portion of transport – such as rail, barge, and short sea shipping – is inherently scheduled, not chartered, and must be integrated accordingly. Finally, FMaaS is an open market where the visibility of the transport service offer for the shipper is global and not limited to contracts between the platform operator and the service suppliers. The applicability of FMaaS presents barriers and questions that open possibilities for a rich multidisciplinary research agenda. One of the main barriers to this concept is the acceptance of the actors involved, along with the lack of scientific evidence on how a user-centric platform system can help achieve the sustainability challenge. Also, the development of centralized platforms may pose serious commercial and legal threats. This paper aims to describe the requirements and possible research avenues of this new paradigm in the wake of an emerging market.

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.

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.

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.