The increasing frequency of extreme weather events poses significant challenges to inland waterway transport, a vital mode of port-hinterland connectivity in Europe. Low water levels disrupt vessel operations by reducing their transport capacity. This study investigates the potential of transfer hubs along the Rhine-Alpine corridor to address this challenge and maintain efficient cargo flows during drought conditions. It offers a replicable methodology for evaluating hub placement and transport resilience, incorporating multi-criteria decision-making and transport modeling. Using a transport competition model, three scenarios, including baseline, drought, and transfer hub application, are simulated to evaluate the cost-effectiveness of implementing these hubs at strategic locations. The integration of hubs, particularly near Duisburg and Andemach, demonstrated the potential to sustain 80% operability during disruptions by enabling modal shifts and alleviating bottlenecks. While competition in the North-Western European hinterland, involving ports such as the Port of Rotterdam, Hamburg, and Antwerp, is further intensified by climate-induced disruptions, our results show that transfer hubs can help ports secure their competitive advantages by ensuring reliable cargo flow under adverse conditions. Our findings highlight the strategic importance of transfer hubs in mitigating climate impacts, enhancing port competition, and supporting sustainable hinterland logistics.

This paper tackles the growing challenges in urban logistics by presenting an optimal distribution network that integrates urban waterways and last-mile delivery, tailored for cities boasting extensive waterway networks. We examine Amsterdam's city center as a case study, prompted by the strain on quay walls, congestion, and emissions, urging a reevaluation of its urban logistics design. We formulate the problem as a two-echelon location routing problem with time windows and introduce a hybrid solution approach for effective resolution. Our algorithm consistently outperforms existing methods, with a superior solution quality, demonstrating its effectiveness across established and newly developed benchmark instances. In our case study, we evaluate the benefits of transitioning from a roadway-centric to a waterway-based system, showcasing significant cost savings (approximately 28 %), reductions in vehicle weight (approximately 43 %), and minimized travel distances (approximately 80 %) within the city center. The integration of electric vehicles enhances environmental sustainability, resulting in a total daily emission reduction of 43.46 kg. Our study underscores the untapped potential of inland waterways in easing urban logistics challenges. Inspired by Amsterdam's experience, global cities can adopt innovative approaches for sustainable logistics, providing valuable insights for managers striving to enhance efficiency, cut costs, and promote sustainable transportation practices.

The weirs in the Meuse river in the Netherlands are after 100 years end of technical lifetime. As a consequence, Rijkswaterstaat is planning renovation or complete replacement. The present weir openings of 60 m wide are used for transit of vessels at high river discharges, when the weirs are lowered. Based on agreements between Belgium and the Netherlands from 1839 [1] and 1843 [2], the possibility of sailing through the weirs during high river discharges should remain (principle of non-deterioration). For the case of replacement, Rijkswaterstaat had a preference for a weir with three openings, for reasons of maintenance and water management. The Dutch MARIN institute executed fast time- and real time simulations to get insight in the navigability of a weir, with openings of 38, 38 and 24 m wide. Results were also used for improvement of the Dutch Guidelines for waterways 2020 [3]. The weir at Sambeek was taken as representative for the other Dutch weirs in the Meuse; 3D flow charts were delivered by the Dutch Deltares institute. The MARIN research showed, that the configuration studied was not feasible; recommended was a middle weir opening of at least around 50 m wide, corresponding with the swept path approaching the weir of 36 m plus ½B at both sides.

A key challenge in the energy transition for Inland Water Transport is the functional design of bunker networks and first-order dimensioning of individual bunker stations. A fundamental ingredient for this is an improved understanding of how upstream energy supply (‘well-to-bunker-station’) and downstream demand (‘bunker-station-to-tank’) may interconnect. In this paper we discuss an approach to the design of bunkering networks that takes logistic modelling to estimate network scale energy demand as a starting point. Depending on the vessels that use the network and the anticipated fuel mix for the overall fleet, logistical modelling may be used to estimate the magnitude of the energy demand along the network. Estimates of the operational range of vessels per energy carrier help to estimate maximum bunker station inter-distances. Insight into the potential supply chains that connect the source of each energy carrier to a physical bunker facility is needed to close the loop. Energy carriers may be needed on board in a gaseous or liquid form, or in the form of electrons. Transfer may take place in the form of loading (e.g., filling the fuel tank, charging the battery pack) or swapping (e.g., exchanging fuel containers, exchanging battery containers). Depending on the energy carrier, transfer method(s) and demand quantities, functional designs of bunker stations (in terms of required system elements and their order-of-magnitude dimensions) can be made. Depending on service level requirements both the dimensions of individual bunker stations and their spread over the network may be optimized. Key contribution of this work is a thorough overview of aspects that play a role in the design of bunker infrastructure for the decarbonisation of inland shipping. Based on this overview steps for further research are recommended.

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.

Purpose: This paper aims to address a location-distribution-routing problem for distributing relief commodities during a disaster under uncertainty by creating a multi-stage model that can consider information updates during the disaster. This model aims to create a relief network that chooses distribution centers with the highest value while maximizing equity and minimizing response time. Design/methodology/approach: A hybrid algorithm of adaptive large neighborhood search (ALNS) and multi-dimensional local search (MDLS) is introduced to solve the problem. Its results are compared to ALNS and an augmented epsilon constraint (AUGMECON) method. Findings: The results show that the hybrid algorithm can obtain high-quality solutions within reasonable computation time compared to the exact solution. However, while it yields better solutions compared to ALNS, the solution is obtained in a little longer amount of time. Research limitations/implications: In this paper, the uncertain nature of some key features of the relief operations problem is not discussed. Moreover, some assumptions assumed to simplify the proposed model should be verified in future studies. Practical implications: In order to verify the effectiveness of the designed model, a case study of the Sarpol Zahab earthquake in 2017 is illustrated and based on the results and the sensitivity analyses, some managerial insights are listed to help disaster managers make better decisions during disasters. Originality/value: A novel robust multi-stage linear programming model is designed to address the location-distribution-routing problem during a disaster and to solve this model an efficient hybrid meta-heuristic model is developed.

Next to environmental aspects, establishing areas for safe and economically viable automated driving in mixed-traffic settings is one major challenge for sustainable development of Autonomous Vehicles (AVs). This work investigates safety in the interactions between AVs, human-driven vehicles, and vulnerable road users, including cyclists and pedestrians, within a simulated urban environment in the Dutch city of Rotterdam. New junction and pedestrian models are introduced, and virtual AVs with an occlusion-aware driving system are deployed to deliver cargo autonomously. The safety of applying this autonomous cargo delivery service is assessed using a large set of Surrogate Safety Indicators (SSIs). Furthermore, Macroscopic Fundamental Diagrams (MFDs) and travel time loss are incorporated to evaluate the network efficiency. By assessing the impact of various measures involving Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Everything (V2X) communications, infrastructure modifications, and driving behavior, we show that traffic safety and network efficiency can be achieved in a living lab setting for the considered case. Our findings further suggest that V2X gets implemented, new buildings are not placed close to intersections, and the speed limit of non-arterial roads is lowered.

Autonomous vehicles (AVs) have been successfully applied in closed environments such as ports and industrial zones, while their operation in open areas has a long way to go. The current research is initiated to overcome this limitation by the introduction of platooning as a transfer mode. It investigates a container transportation problem between a port and an industrial area where the platform facilitates collaborative transportation. Both zones are appropriate for automated driving, whereas their connecting route is not. Different carriers are present at the port, and each transportation task can be done either by a truck or an AV. The platform not only operates as the interface between demand points and carriers but also provides a platooning service to move AVs through non-autonomous roads. It specifies the transportation schedules and service fees based on which the carriers will decide whether to use AVs or trucks for each transportation task. This is modeled as a Stackelberg competition, transformed into a conventional mixed-integer model, and solved to optimality. The approach enables demand and resource pooling between the port and industrial area. Numerical results show that the successful application of AVs highly depends on platoon formation costs and regulations.

While the procurement decision is generally made by individual buyers, this study investigates how a group of buyers can make a shared decision. We call this collaborative approach, co-procurement. A mathematical model is formulated for the decision of procurement from multiple suppliers. The model is solved for individual buyers. The outcome shows the optimal number of items a buyer should buy from different suppliers such that the total cost is minimised for that buyer. Next, it is investigated how a group of buyers could make this decision together. The proposed model takes into account transaction costs of collaboration, to determine the optimal size of the collaboration and the involved parties. The idea is new in the old direction of procurement and it introduces the concept of transaction costs in this area and analyses its impact on the optimal collaboration size and mix. A case study from Dutch Food Valley is provided to investigate the benefits of co-procurement and validate the developed structure. The results indicate that co-procurement can bring considerable cost-savings through consolidation of orders and more efficient transportation schedules. A sensitivity analysis is conducted to determine the impact of changes in the transaction cost in favour of the co-procurement.

Automated ground vehicles (AGVs) are essential parts of container operations at many ports. Forming platoons—as conceptually established in trucking—may allow these vehicles to directly cater demand points such as dry ports in the hinterland. In this work, we aim to assess such AGV platoons in terms of operational efficiency and costs, considering the case of the Port of Rotterdam. We propose a multi-objective mixed-integer programming model that minimizes dwell and idle times, on the one hand, and the total cost of the system involving transportation, labor, and platoon formation costs, on the other hand. To achieve Pareto optimal solutions that capture the trade-offs between minimizing cost and time, we apply an augmented epsilon constraint method. The results indicate that all the containers are delivered by AGVs. This not only shortens the dwell time of the containers by decreasing loading/unloading processes and eliminating stacking but also leads to considerable cost savings.