Data-Driven Hull-Fouling Cleaning Schedule Optimization to Reduce Carbon Footprint of Vessels

Abstract

In response to climate change, the International Maritime Organization has introduced regulatory frameworks to reduce greenhouse gas emissions from international shipping. Compliance with these regulations is increasingly expected from individual shipping companies, compelling vessel operators to lower the CO2 emissions of their fleets while maintaining economic viability. An important step towards achieving this is performing regular hull and propeller cleaning; however, this entails significant costs. As a result, assessing whether ship performance has declined sufficiently to warrant cleaning from an environmental and economic standpoint is a critical task to ensure both long-term viability and regulatory compliance. In this paper, we address this challenge by proposing a novel data-driven dynamic programming approach to optimize vessel cleaning schedules by balancing both environmental and economic considerations. In numerical experiments, we demonstrate the usefulness of our proposed methodology based on real-world sensor data from ten tramp trading vessels. The results confirm that over a four-year period, fuel consumption can be reduced by up to 5%, even when accounting for the costs of one or two additional cleaning events.