This study investigates the implications of alternative energy adoption for inland vessels at both the system and operational levels. At the system level, hybrid powertrain sizing and energy demand estimation are based on confined-water hydrodynamics and expected operational profiles. At the operational level, scenarios of bunkering station distribution along discretised nodes of the Rotterdam - Antwerp corridor as a use case are analysed using a network-based approach coupled with mixed-integer linear programming (MILP). Results show that a vessel’s operational profile and the spatial deployment of refuelling infrastructure strongly influence energy storage requirements. These insights provide a foundation for optimisation and trade-off analyses, supporting informed decision-making for the sustainable transition of inland navigation.

Anticipatory governance supports mission-oriented innovation policy by identifying, mitigating, and preparing for barriers that impede socio-technical transformations. While recent research introduced the Mission-Oriented Transition Assessment as a formative approach to mission governance, we insufficiently understand how this approach helps govern missions over a sustained period. This study applies a ‘real-time’ Mission-Oriented Transition Assessment to yield longitudinal insights into how mission barriers are foreseen, constructed, and responded to by stakeholders. We do so in the context of the Dutch maritime mission ‘Climate neutral shipping by 2050′. The results of 14 assessments over a period of 1.5 years with 124 stakeholder representatives show how 19 mission barriers are collectively anticipated, explicated, and acted upon. As such, this paper conceptualizes and empirically explores the usefulness of a ‘real-time’ Mission-Oriented Transition Assessment as a formative approach to anticipatory mission governance.

The Resource Constrained Project Scheduling Problem with a flexible Project Structure (RCPSP-PS) is a generalization of the Resource Constrained Project Scheduling Problem (RCPSP). In the RCPSP, the goal is to determine a minimal makespan schedule subject to precedence and resource constraints. The generalization introduced in the RCPSP-PS is that, instead of executing all activities, only a subset of all activities has to be executed. We present a model that is based on two graphs: one representing precedence relations and one representing the activity selection structure. The latter defines which subset of activities has to be executed. Additionally, we present theoretical properties of this model and give an exact solution method that makes use of these properties by generating cutting planes and setting bounds on variables. Furthermore, three problem properties are introduced to classify problems in the literature. We compare our model to a model from literature on instances that possess a subset of these three problem properties and find a reduction in computing time. Furthermore, by comparing results on instances that possess all problem properties, it is shown that the computing times are decreased and better lower bounds are found by the cutting planes and variable bounds presented in this paper.

The International Maritime Organization aims to achieve full decarbonization by 2050 in response to climate change. This ambitious goal demands well-defined strategies guided by techno-economic assessments. The complexity of global shipping systems makes predicting long-term maritime trade patterns challenging, necessitating scenario-building rather than precise forecasts. Investigating shipping demand scenarios is crucial due to the uncertainty brought by the energy transition and its role as the primary driver of shipping emissions. This paper improves the representation of maritime shipping in Integrated Assessment Models (IAMs) by examining the impacts of climate targets on future shipping demand. A novel econometric model, grounded in advanced gravity theory and integrated with machine-learning algorithms, is proposed to estimate the elasticities of variables in bilateral seaborne trade. By coupling this model with the WITCH IAM, we explore various scenarios, providing deeper insights into trade patterns and their implications. The results show that stricter climate policies and higher carbon taxes reduce GDP due to higher abatement costs, higher fuel prices, and therefore reduced seaborne trade, especially for oil products and containerized cargo. Early adoption of carbon taxes in Europe may shift oil production and consumption patterns, temporarily boosting seaborne trade. Sub-Saharan Africa could experience significant demand growth due to economic and population increases.

Inland waterway vessels are critical to the hinterland transportation network, offering an environmentally friendly alternative to road and rail transport. However, climate change poses significant challenges, such as fluctuating water levels and extreme shallow water conditions that lead to increased resistance and reduced propulsive efficiency. These conditions necessitate innovative design and operational strategies to ensure the efficiency and sustainability of propulsion systems. Given the increase in resistance and risk of propeller emergence in shallow water conditions, this study explores the development of climate-resilient inland vessels, by implementing the distributed thrust concept, where multiple smaller propellers replace conventional single relatively large units, offering superior maneuverability, propeller load distribution, and adaptability to varying water depths and conditions. Utilising state-of-the-art resistance approximation and a robust optimisation method, this research proposes a novel shallow-water model that enables optimal configuration of propeller size, number, and placement, considering key performance metrics such as thrust efficiency and ventilation mitigation, contributing to sustainable inland waterway transportation. Results from a case study demonstrate that the distributed propulsion system can effectively shift the operational threshold for propulsion, extending the navigational capabilities and performance in water depths where conventional design would face limitations. The findings highlight the potential of integrating distributed propulsion with advanced optimisation techniques to address climate-induced challenges while ensuring operational reliability.

The integration of methanol power, propulsion and energy systems (PPE) generates uncertainties linked to the selection and sizing of systems, layout design and compliance with strict safety regulations. This paper argues that alternative fuels, such as methanol, should be treated as disruptive innovations, in part due to the uncertainties linked to their implementation. These uncertainties strongly connect to the PPE dimensions and the dependencies among the systems because of integration requirements. Through a model based system engineering inspired approach, the uncertainties are elucidated into relevant inputs for the proposed framework. The authors introduce an uncertainty evaluation framework that uses Monte Carlo simulations to generate the layout design space under uncertainty. The impact of uncertainty on the design is examined through a case study on the layout of a notional engine room. Multiple probability distributions for the PPE dimensions and varied logical architectures – reflecting systems dependencies – are applied to identify patterns in the generated design space. The varied logical architectures influence drastically the dominating solutions of the design space regarding the length. For a 1000-kW notional vessel, under the varied scenarios, the length of the engine room clustered in specific values while the connection costs produced wide value spectrum.

Shipbuilding, a pivotal industry supporting shipping, fishing, wind energy, and defence, confronts global competitive pressures amidst contemporary challenges. Despite its significance, the sector faces ongoing challenges in achieving digital maturity. This study is part of a research that aims to expedite the shipbuilding digital transformation, particularly by identifying current applications of key enabling technologies (KETs) in the shipbuilding activity through a comprehensive literature review. The KETs are first identified, then they are categorized based on two criteria: the main focus of the technology and the specific function in the shipbuilding process. While the analysis reveals an extensive quantity of applications (88), they are rather scattered and do not present a strong trend, predominantly relying on traditional approaches and backing mass production-like processes. It is also shown that the applications of KETS in the shipbuilding industry is still very immature, with only 15% of applications in the deployment phase, while the vast majority remain in the conceptual or development phases. Moreover, this study highlights the interconnected nature of these KETs, that point to the need to support shipyards in setting key priorities and strategies for their implementation. The study concludes by proposing avenues for future research to address these challenges and boost the shipbuilding industry towards its digital transformation.

In large modular construction projects, such as shipbuilding, multiple similar projects arrive stochastically. At project arrival, a schedule has to be created, in which future modifications are difficult and/or undesirable. Since all projects use the same set of shared resources, current scheduling decisions influence future scheduling possibilities. To model this problem, we introduce the Dynamic Resource Constrained Multi-project Scheduling Problem with Static project Schedules. To find schedules, both a greedy approach and simulation-based approach with varying scenarios are introduced. Although the simulation-based approach schedules projects proactively, the computing times are long, even for small instances. Therefore, a method is introduced that learns from schedules obtained in the simulation-based method and uses a neural network to estimate the objective function value. It is shown that this method achieves a significant improvement in objective function value over the greedy algorithm, while only requiring a fraction of the computation time of the simulation-based method.

Requirements elucidation is a significant part of early-stage ship design, especially in naval architecture for complex ships. During a vessel’s acquisition process, the stakeholders propose requirements in statements, regulations, Concepts of Operations (CONOPS), vignettes, and Minutes of Meetings, all expressed in natural language. However, bridging these natural language requirements and their impact on the final design remains an open research problem. This research proposes a framework that utilizes semantics interpretation to map the natural language requirements (R) to the layers of the systems architecture: Functional (F), Logical (L), and Physical (P). This paper proposes to use semantics to better understand the effect of requirements on design change occurring in the logical and physical architecture layers of the system architecture. This research also introduces the classification of the requirements on a two-dimensional axis system, with one axis being their importance to the stakeholders and the other axis evaluating their elasticity (i.e., if they can be interpreted in more than one way). This classification provides insights into the characteristics of requirements that may impact the physical design. The proposed framework shows potential for identifying and tracing the propagation of changes and uncertainties stemming from the requirements to the other layers of the systems architecture. This paper showcases the framework through a case study on the semantic interpretation of redundancy and safety regulations for the design of a short-sea vessel’s engine room. The results show that hard” and ”elastic” safety requirements are more influential on the layout arrangement and
thus the shape of the generated design space.

Ship pipe route design is often overlooked in the context of the energy transition, although it is a crucial driver for design time and costs. Motivated by this, we propose a mathematical approach for modeling uncertainty in pipe routing with deterministic optimization, stochastic programming, and robust optimization. The uncertainty entails not knowing which type of fuel will be used in the ship's future. All three models are based on state-of-the-art integer linear programming models for the Stochastic Steiner Forest Problem and adjusted to the maritime domain using specific constraints for pipe routing. Our results highlight the importance of accounting for uncertainty in ship pipe routing, demonstrating cost reductions of up to 22% based on experiments with artificial and realistic data. Our methods enable engineers to explore different levels of preparedness for the energy transition with minimal effort during the early design phase.

Retrofits to alternative fuels like methanol represent a strong candidate for complying with current and upcoming environmental regulations. The decision to retrofit to methanol power, propulsion and energy systems introduces uncertainties linked to technology integration, conversion costs, and environmental performance. This paper proposes a bi-objective Markov decision process as the foundation of a design tool aimed to support retrofit decisions within a vessel’s lifecycle. This approach aims to quantify the trade-off between the conflicting objectives of (a) emissions reduction and (b) retrofit costs minimization. The bi-objective formulation is evaluated against the equivalent single-objective formulations of the problem to assess the effect on the feasible design pathways. Depending on the initial vessel preparation level for a retrofit, this paper quantifies the uncertainty in the retrofit pathways during the lifecycle through the metrics of optimal state and optimal action. A case study has been applied to a notional short-sea vessel. Results indicate differing solutions when comparing the bi-objective to two individual single-objective scenarios. Preparation for a retrofit and the selection and usage of a methanol dual fuel configuration indicate a promising strategy to satisfy both objectives for the vessel’s lifecycle timespan.

This research addresses the importance of sustainability in shipping beyond fuel selection, stressing the need for responsible material usage in vessel construction and maintenance. Transitioning to a circular economy is crucial for sustainable waste management in the industry, yet current ship design neglects circularity considerations, prioritising functionality and cost. The research evaluates frameworks such as the butterfly diagram, Cradle-to-Cradle, 10R, and ReSOLVE to integrate circularity into ship design. Combining the 10R framework with the Material Circularity Indicator method, this study offers practical insights for circularity in ship design. Challenges include integrating these methods into standard design processes, which are mitigated by fusing 10R strategies with systems engineering. A case study on wheelhouse redesign demonstrates the effects of this approach, highlighting the importance of supplier collaboration for circularity enhancement.

Within the maritime transport sector, data and information have the potential to change the way we work for the betterness. Increasingly shipping companies are generating, storing and reporting data, on their position, their engines, their products and their emissions. As a result, data is sometimes referred to as the new gold and people often protect it in the same way. However, unlike gold, data has actually more value when it is shared and used in collaboration with others. [...]

Inland water vessels are impacted by climate change in two respects. First of all, they will need to convert to low-impact power propulsion and energy (PPE) systems. Secondly, they will need to deal with the impact of climate change, especially longer periods of very low and high water. This paper reviews the multi facet impacts of climate change on inland waterway vessel performance and problems associated with the choice of alternative power energy and propulsion (PPE) system on the vessel’s performance.

The current sea margin estimate applied in early ship design, commonly assumed 15-20% extra installed engine power, is not based on calculations, but has nonetheless become an industry standard. These sea margin estimations, applied in early ship design, are insufficiently accurate. This paper evaluates if a data driven approach is suitable to more accurately predict the sea margin in early ship design. Using operational data this method considers the whole operational profile of the vessel not limited to design or calm water conditions. A case study is performed where a data driven model is trained to make power predictions, subsequently this trained model is used to make calm water predictions. This proof of concept illustrates the potential of proposed method to be utilised for sea margin estimations in early ship design.

With space constraints onshore, strong renewable resources available far offshore and growing green hydrogen demand, far offshore green hydrogen production may be an attractive option. To assess this potential, a mixed integer quadratically constraint programming (MIQCP) optimization model was developed to find the cost per kilogram of far offshore green hydrogen in specific scenarios. The design of the far offshore green hydrogen supply chain was optimized with this model for six high potential scenarios in varying locations and the results were analyzed. It was found that far offshore green hydrogen costs are in the same order of magnitude as the costs of its alternatives. Far offshore green hydrogen may be considered marginally competitive with these alternatives from 2035 onwards in the analyzed scenarios when taking into account the considerable advantages of far offshore production, such as avoidance of scarce land usage in crowded areas and certain geopolitical considerations.

The ongoing technological development of methanol energy converters (EC) towards decarbonization means that their dimensions and performance characteristics will be continually updated during the lifecycle of vessels currently designed. These advancements influence the ease of EC integration within the general arrangement of the vessel. The decision to switch from an internal combustion engine to a fuel cell or a hybrid configuration depends both (1) the technology adoption costs (i.e. CAPEX, OPEX) of the EC and (2)
on the effect of EC on the actual engine room layout. The state-of-the-art literature has typically addressed these two challenges separately. This study proposes a design method to bridge these two fields by combining the use of (1) Markov decision processes to assess uncertain future methanol EC developments during the vessel lifecycle and (2) a generative probabilistic layout algorithm to quantify the risks associated with the EC systems layout integration. The case study identifies the drivers behind the EC technology choice during the lifecycle of a notional yacht vessel.

Shipowners need to prepare for low-emission fuel alternatives to meet the IMO 2050 goals. This is a complex problem due to conflicting objectives and a high degree of uncertainty. To help navigate this problem, this paper investigates how methods that take uncertainty into account, like robust optimization and stochastic optimization, could be used to address uncertainty while taking into account multiple objectives. Robust optimization incorporates uncertainty using a scalable measure of conservativeness, while stochastic programming adds an expected value to the objective function that represents uncertain scenarios. The methods are compared by applying them to the same dataset for a Supramax bulk carrier and taking fuel prices and market-based measures as uncertain factors. It is found that both offer important insights into the impact of uncertainty, which is an improvement when compared to deterministic optimization, that does not take uncertainty into account. From a practical standpoint, both methods show that methanol and LNG ships allow a cheap but large reduction in emissions through the use of biofuels. More importantly, even though there are limitations due to the parameter range assumptions, ignoring uncertainty with respect to future fuels is worse as a starting point for discussions.

Editorial.

Accurate cost estimates are essential for staying in business in a competitive shipbuilding industry. With new technologies and the energy transition creating an ever-changing landscape, traditional cost estimation methods based on product specifications can no longer keep pace. The need for improvement especially arises for Engineering-To-Order projects, considering that the profit margins are narrow. The use of process information in the estimation process could increase the reliability and flexibility of these estimations. This article presents a concept that utilizes graph theory to include process information in the cost estimations applied to steel buildings. This concept is specifically suited for the early stages of Engineering-To-Order projects.