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 regulations to reduce greenhouse gas emissions from international shipping. Compliance with these regulations is increasingly expected from individual shipping companies and can be achieved through more frequent hull and propeller cleanings. Since this maintenance work entails high costs, vessel operators are confronted with the trade-off between lower fleet emissions and better economic performance. As a result, assessing whether fuel efficiency has declined sufficiently to warrant cleaning from an environmental and economic standpoint is a critical task. To address this challenge, this paper proposes a novel dynamic-programming-based algorithm to optimize vessel cleaning schedules. The main components of the optimization problem are a data-driven function for the fuel consumption costs and an additive term that sums up the costs per vessel cleaning. In numerical experiments, we demonstrate the usefulness of our proposed methodology based on real-world sensor data from nine tramp trading vessels. The results confirm that, over four years, fuel consumption can be reduced by up to 5%, even when accounting for the costs of the additional cleaning events.