Enabling ship changeability; a lifecycle approach to the maritime energy transition

Abstract

The maritime energy transition, characterised by the shift toward alternative fuels to reduce emissions, presents the maritime industry with a complex and uncertain decision-making problem. It is complicated by multiple external factors, including the uncertain development of emission-reduction regulations, the performance of emission-reduction measures, and the availability of necessary infrastructural- and economic support. Further challenges arise from maritime industry-specific characteristics, such as the capital-intensive nature, long lifecycles, differences in operational requirements and the technical complexity of ships.

From the perspective of the vessel-level decision-maker, these interdependent and continuously evolving factors create deep uncertainty in emission-reduction decisions. For many, this resulted in a decision paralysis that is reflected in postponed fleet renewal investments, and the ageing of the global fleet. Consequently, the main research question this thesis addresses is: How can decision-making in the maritime energy transition be supported to enable timely ship design- and retrofit decisions under deep uncertainty? To address the deep uncertainty in the maritime energy transition, this thesis explores how to enable the use of changeability as a strategic response. This shifts the perspective from reactive compliance to strategic preparation, increasing awareness of when, what, and how to adopt emission-reduction measures.

A literature review categorises decision-making challenges and proposes a theoretical framework that subdivides the decision space into a context space, object space, and value space, including the mappings between them. Within these spaces, two primary challenge categories are identified: complexity and uncertainty. Although conceptually distinct, their interaction can result in deep uncertainty, reinforcing decision paralysis. Building upon this foundation, the Framework for Exploration of Adaptive Robustness (FEAR) was developed to support vessel-level decision-makers. The framework structures the decision problem into three interconnected modules: What, How, and When, which are used to iteratively explore the integration of emission-reduction systems.

The What-module investigates alternative emission-reduction measures and the required modifications to the ship system architecture. System representations are constructed using models from a system library, and system architecture evolution is analysed using graph and set theory to compare alternatives qualitatively and quantitatively. The How-module addresses the integration of system architectures and their changeability within the constraints of ship design. An automated ship layout methodology has been developed that explicitly incorporates system changeability considerations. This method quantifies the trade-offs between preparatory investments and adaptation costs, and identifies investments that reduce future retrofit expenditures.

The When-module evaluates emission-reduction pathways under uncertainty using adaptive robust optimisation. The optimisation is used to investigate which initial and retrofit selections of emission reduction measures remain robust under uncertain fuel costs and emission taxation, thereby providing insight into the value of changeability throughout the ship design lifecycle.
The modules are combined into the FEAR framework, which can be used to iteratively explore alternative system architectures and changeability during the concept design phase. As new technologies and information become available, the framework can be reapplied, enabling continuous evaluation of emission-reduction strategies and previously integrated change enablers. The practical use of the framework is investigated through a case study.

Incorporating change enablers during the initial design phase resulted in approximately 20-46% reduction in relative material and labour retrofit costs compared to a design without future preparation. This reduction is further influenced when accounting for lost revenue, retrofit timing, and additional yard costs. The results from the case study were discussed in an interview with expert designers, they agreed that it offers valuable tools to explore alternative emission-reduction measures and system- and ship-level preparations. The FEAR was found to be mainly beneficial to support decision argumentation. However, they also noted that the current form is not yet applicable in practice, as it requires a dedicated interface and further validation across multiple vessel types and system architectures.

In conclusion, FEAR provides a theoretically substantiated, practical framework for structuring decision-making under deep uncertainty. By integrating considerations of existing alternatives, how they can be prepared for, and when they should be implemented, the framework enables proactive and adaptive decision-making in the maritime energy transition.