Ports are actively pursuing greater operational efficiency to handle the increasing global flow of goods, while simultaneously improving the energy efficiency of their operations to comply with new environmental regulations. As a result, innovation-leading ports have begun to recognize the potential of digital twins to monitor, coordinate, and optimize port processes, enabling energy savings and reductions in both costs and CO2 emissions. Although digital twins have gained significant momentum in other domains, such as smart manufacturing and aerospace, their adoption in ports remains challenging. This can be explained by the multi-stakeholder nature of ports and the high complexity of their interconnected processes, requiring decision-making across organizational boundaries.
Grounded in the port context, this thesis examines what constitutes a digital twin, proposes a framework to assess the maturity of existing port digital twins, and develops modeling and explainable AI-enabled decision support components for port and maritime operations. These components span seaside, quay, yard, and gate processes and can serve as building blocks of future port digital twin implementations. The thesis consists of six papers:
Paper 1 provides an in-depth literature review of digital twins across multiple domains and transfers insights from these to the port domain. The paper outlines how digital twins can enhance operational efficiency and support energy savings in ports. It also identifies the characteristics and design requirements that a port-specific digital twin must fulfill. Based on these findings, the paper proposes a tailored definition of a digital twin for the port domain.
Paper 2 discusses how digital twins’ maturity can be assessed within six maturity levels and presents milestones for their implementation. Notably, Interoperability is identified as the highest maturity level,as the numerous stakeholders and their respective digital twins must work together to reach a coordinated system of systems performance. Using this assessment demonstrates that only a few innovation-leading ports have developed sophisticated digital twinning solutions so far.
Paper 3 focuses on container retrieval, balancing two competing objectives: minimizing yard crane moves and adhering to tight truck scheduling. This reflects the conflicting perspectives of different stakeholders in the port context. The provided optimization model and heuristic algorithm demonstrate that addressing both problems simultaneously may result in reduced efficiency of the individual objectives. However, from a systems perspective, this approach leads to higher overall port efficiency.
Paper 4 examines quay cranes at the system level by developing an explainable AI framework to predict whether a quay crane will experience a breakdown during vessel operations. Using monitoring data, operational data, and weather observations, the study identifies how operational intensity, hoist-related warning patterns, and environmental conditions jointly influence the likelihood of a breakdown. This system-level predictive capability enhances situational awareness and enables early identification of disruptions.
Paper 5 builds on Paper 4 by focusing on the prediction of individual critical error events. Rather than assessing the overall likelihood of a breakdown, the model identifies which error type is likely to occur next and estimates its timing. Using eXtreme Gradient Boosting with lagged error sequences, operational data, and weather conditions, the study offers component-level insights that complement the systemlevel prediction in Paper 4 and support more targeted maintenance interventions.
Paper 6 expands the perspective beyond ports by analyzing fuel consumption in inland ferry operations using GPS-derived trip legs and journeys enriched with environmental data. Combining unsupervised clustering to uncover operational patterns with supervised learning and SHAP-based explainability, the study identifies operational speed as the dominant driver of fuel consumption and links consumption patterns to individual captains’ driving behavior. This contributes to maritime decision-making by enabling targeted interventions such as eco-driving strategies.
Together, these six papers contribute a conceptual grounding of port digital twins, provide a tool for their assessment, and provide modeling components to aid in port and maritime decision-making.