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.

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.

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.

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.

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.

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.

In the fiercely competitive shipbuilding industry, precise cost estimates must be considered as they serve as a critical input for determining market prices effectively and ensuring a small profit for the shipyard. In Western Europe, where most projects are Engineering-To-Order (ETO), cost estimations are extra challenging, due to lack of similarity between projects. On top of this, cost estimations are becoming increasingly difficult in a market confronted by mounting challenges related to safety regulations, cost-effectiveness, and the pressing need to address energy conservation and environmental protection. New technologies introduce changes in nearly all aspects of shipbuilding design and construction. This article conducts a literature review, to present the state-of-the-art methods for estimating man-hours, explicitly focusing on man-hours for shipyard production, excluding overhead costs and challenges the suitability of existing systems for ETO and especially for the changes caused to ships by the energy transition. An indication is given of the practicality of each method as outlined in the literature. A solution direction, incorporating the construction process, is proposed to improve cost estimations for ETO projects in pre-contract phase.

As climate change continues to pose a significant threat to our planet, international maritime shipping plays a crucial role in mitigation efforts. Recognizing the urgency, the International Maritime Organization (IMO) has revised its targets, now aiming for full decarbonization by 2050. However, there is no established pathway to get to the target. To achieve this, there is a need for models depicting possible futures of the maritime sector, and finding feasible pathways. This research aims to find the most suitable way to develop models to find pathways toward decarbonization targets. This involves evaluating existing ranges and scenarios to understand current estimations and their underlying assumptions and assessing the most suitable modeling methods based on defined criteria. Considering the context, the most suitable models for this objective should perform on a global scale. They should include dynamics between shipping demand & supply as well as the derived fuel demand and supply and emissions; integrate the sector with other parts of the economy; incorporate various technologies into the framework; and span multiple scenarios. The study has two main parts. First, existing scenarios on the future of maritime shipping are analyzed to identify current estimations and assumptions impacting these estimations. Second, various modeling frameworks are assessed against the defined criteria to identify the most suitable modeling structure for achieving the decarbonization targets. Many projections do not meet the IMO’s updated targets, highlighting the need for a paradigm shift in setting targets and finding feasible pathways rather than focusing solely on individual measures. Integrated Assessment Models (IAMs) have been identified as suitable for such projections and policy analysis, although international shipping is often underrepresented in current models. Future research should combine the insights of sectoral models in integrated frameworks such as IAMs to develop integrated strategies to investigate pathways to achieve zero-emission targets. The ultimate goal is to understand how to effectively reduce the sector’s emissions and achieve more environmentally friendly international maritime shipping.