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Offshore wind energy is recognised globally as a viable alternative to finite energy sources. However, large cost reductions are still needed, particularly in the Operations & Maintenance (O&M) phase, which currently accounts for about 30% of the cost of offshore wind. For large component replacements, a jack-up vessel is often leased from the spot market, resulting in high costs and low utilisation. These costs can be lowered when multiple wind farm service providers would share the resources needed to employ jack-up vessels. In this paper, we analyse two types of resource sharing, as an alternative to each service provider leasing its own vessel: (i) vessel purchasing and sharing and (ii) the combined use of vessel and harbour sharing. We design a simulation model and include stochastic processes such as weather patterns and component failures. Results show that cost benefits up to 45% can be achieved compared to a leasing policy, depending on the number of wind farm service providers involved and on the geographical distance between offshore wind farms. Moreover, it is shown that the jack-up vessel should not be fully utilised to minimise costs. The performance benefits of harbour sharing in addition to vessel sharing are generally small, but become more significant if the network faces considerable congestion. Results are illustrated using a case study based on a setting in the Western North Sea.

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.

Increase of congestion at container deep seaports and shortage of capacity has led inland transport systems worldwide to rely more and more on inland terminals, and on the use of high capacity modes of transport to generate economies of scale and reduce negative effects of trucking. In this setting, planning the transport of maritime containers between a deep seaport and a final inland destination must also consider due dates and soft time windows, the latter known as Demurrage and Detention (D&D). In this paper, we formalize the concept of D&D, model the multimodal planning problem, and assess the impact of different D&D regimes on the emerging inland transport systems. By means of an experimental framework, we compare different D&D policies and provide managerial insights. The experiments highlight the effects of existing D&D regimes on transport efficiency and provide guidelines for their choice in practice. D&D are shown to have a twofold effect: first to limit consolidation opportunities and force the use of trucks as buffer, and second to push containers to dwell unnecessarily at the seaports.

In many countries worldwide, inland shipping is a reliable and viable option to transport maritime containers inland. This modality comes with a set of operational challenges, among which the stowage of containers, i.e. the positioning of a set of containers onboard, is a complex and delicate task. In this paper, we develop a DSS to support the generation of feasible stowage plans, with the goal of maximizing the amount of containers onboard, while guaranteeing stability and complying with a set of rules. In this regard, we propose the case of inland shipping in the Netherlands. The stowage problem is modelled with a mixed integer linear programming mathematical formulation. Numerical experiments validate the model and give insights on the complexity of the problem for real-world size instances.

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. (C) 2015 Elsevier B.V. All rights reserved.

In this paper, we discuss, model and heuristically solve a particular container transport problem, related to the inland transport between a sea port and an inland terminal directly connected to it, called dry port. The transport is undertaken by a heterogeneous fleet. Similarly to the classical vehicle routing problem with pickup and delivery (VRPPD), vehicles can deliver containers to the different quays of the sea port and pick up others to bring to the dry port. The highly clustered setting, the strong heterogeneity of the fleet and time constraints add further features to the classical problem. After a detailed description of the setting, we develop the mathematical model and propose a heuristic approach.