The demand for new offshore wind farms is increasing at a rapid pace, and the installation rate must be quadrupled by 2030 to meet the ambitions of European countries. The installation of the superstructures involves several components and is highly weather-dependent, making this an important bottleneck. In this paper, we evaluate the two main strategies for the installation of superstructures: feedering and shuttling. With feedering, the installation vessel is fed with components by feeder vessels directly from manufacturing ports. With shuttling, the installation vessel retrieves the components itself from a marshalling port. In contrast to existing studies, we include manufacturing ports and their production rate to have a better understanding of their influence on the installation rate and develop a rolling horizon optimization-simulation framework composed of a mixed integer linear programming model and a Markov simulation model for weather forecasting. A heuristic is proposed to solve the model to overcome the limitation of commercial solvers. Results indicate that accurate initial buffer calculations, depending on the production rate at the manufacturing ports and project-dependent characteristics, can increase the installation rate significantly for both strategies. Finally, feedering outperforms shuttling in most scenarios and is less weather dependent.

The emergence of Intelligent Transportation Systems (ITS) for freight transport in recent times has created interest among practitioners and researchers to extend freight ITS to support broader logistics processes, including dynamic tour scheduling, loading and unloading, warehousing, and even production. However, connecting transport data, ITS and logistics information systems require collaboration between different organizations and new business models to create business value for logistics actors. It is critical for these stakeholders to consider how their business models connect to create meaningful new data-to-information value chains. This study develops a conceptual framework to identify opportunities for logistics value creation with freight transport data. Building on the literature we construct a framework that reconciles multi-firm and firm-level business modelling. The main component is a generalized framework for Data-to-Value (DtV) chains for applications in information and communications technology. In order to support its business validity, we extend this framework with Business Model Canvases (BMCs) of the actors in the value chain. Three real-life use cases from a freight ITS community in the Netherlands are used to evaluate and illustrate the framework.

High-speed rail (HSR) is frequently seen as a promising alternative for long-distance travel by air and road, given its environmental advantages whilst offering a competitive level of service. However, a European HSR-network is yet to be realised, with the current state amounting to a patchwork of poorly connected subnetworks. Consequently, this results in a suboptimal performance from a user, operator and societal perspective. We present a customised version of the Transit Network Design and Frequency Setting Problem (TNDFSP) for the long-distance transport context and HSR in particular. We apply an adapted version of a heuristic solution approach to analyse the users’, operators’ and societal performance of a European HSR-network by conducting an extensive series of experiments to test the network's performance under various policy priorities and HSR design variables. Our experiment results show that designs resulting from the consideration of externalities yield more extensive networks with larger coverage and modal shifts. For such networks to materialise, high public investments are needed. The obtained network designs contain four different line types, exhibit spatial disparities in network density, and allow for the identification of potential hubs and critical infrastructure. The strong network integration with overlapping and border-crossing lines of substantial lengths highlights the importance of cross-border cooperation and rail interoperability. We hope our findings will contribute to the ongoing public and professional debates on designing an attractive and competitive European HSR-network.

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.

Port terminals on floating modular platforms are a conceivable solution for the problem of limited space and water depths restrictions of ports in estuary regions. A design of a dedicated Transport&Logistic hub has been developed in the scope of the Horizon 2020 project Space@Sea. This paper addresses dedicated options of waterborne hinterland transports and discusses opportunities for bypassing onshore terminals by means of river-sea or sea-going inland vessels. A tailored simulation method for ship operations utilises a specific cost model and is applied to derived demand scenarios. Cargo flow statistics of an onshore port have been projected onto the hub to identify relevant waterborne transports to the hinterland. Three different vessel types are implemented, whereas inland vessels are considered with two different sizes. A comparison of round trip durations and transport costs per transported container between a floating terminal and a relevant hinterland port pointed out, that a non-stop connection with sea-going inland vessels is the economically favourable solution. A feeder vessel is the faster solution in coastal waters but it can not compensate the time saved by omitted terminal visits on a direct hinterland connection.

The scheduling of quay cranes is a core logistics challenge that affects significantly the loading and unloading time of a vessel berthed at a container terminal. In this paper, we study the Stochastic Floating Quay Crane Scheduling Problem involving cranes situated on the quay of an offshore modular platform. Specifically, we consider the case in which each crane is situated on a different module of the platform, thereby confining its operation range. Additionally, we assume stochastic crane productivity rates due to the effect of the offshore wind. To tackle the problem, we propose a simheuristic framework, which combines Iterated Local Search with Monte Carlo Sampling into a joint collaborative scheme. The main objective is to minimize the expected completion time of the loading and unloading process taking into account precedence, nonsimultaneity, non-crossing, and spatial constraints of the problem at hand. The performance of the proposed simheuristic is investigated on a set of established problem instances across different configuration parameters and under various real-world environmental scenarios offering insightful conclusions.

This paper investigates the optimization of biomass terminal equipment deployment. A mixed integer linear programming model is developed and applied to minimize the terminal's investment and operational costs related to dedicated and partially used or shared equipment between a terminal's operational steps. The results minimize annual terminal costs through equipment and infrastructure selection and utilization. Tipping points where the technology and equipment type or size change in relation to the increasing throughput are highlighted. Analytical results emphasize the importance of storage costs in all biomass terminals, as well as the critical influence of operational costs in larger facilities.

Fresh cut flower supply chains are aware of the need for reducing spoilage and increasing customer satisfaction. This paper focuses on a part of the cut rose supply chain, from auction house to several end customers. A new business mode is considered that would allow end customers to subscribe to florists and have a continuous supply of bouquets of roses. To make this business mode feasible, we propose to benefit from real-time information on roses’ remaining vase life. First, a quality-aware modeling technique is applied to describe supply chain events and quality change of cut roses among several supply chain players. Then, a distributed model predictive control strategy is used to make up-to-date decisions for supply chain players according to the latest logistics and quality information. This approach provides a tool for multiple stakeholders to collaboratively plan the logistics activities in a typical cut rose supply chain based on roses’ estimated vase life in real time. The proposed approach is compared with a currently used business mode in simulation experiments. Results illustrate that the new business mode and the planning approach could reduce unmet demand and spoilage in a cut rose supply chain.

Marine litter in port areas has a huge negative environmental impact and poses a risk to vessels. Therefore port authorities are using special vessels for sweeping. Nowadays, these vessels are usually only deployed after complaints on excessive amounts of marine litter. In this paper an innovative routing method is proposed to sweep marine litter in a port area proactively. The routing method is formulated as a mixed-integer programming (MIP) model. In order to test the sweeping model a dynamic model is developed that predicts the locations in the port area where marine litter will accumulate depending on factors like supply, physical dimensions of port compartments and wind directions. To benchmark the performance of the sweeping model simulations are performed comparing the routing method with other more intuitive policies. It is concluded that using the sweeping policy lower litter levels can be achieved at lower costs.