Work

Forward dynamic programming-informed Bayesian method for optimal power allocation in short-sea shipping

Year: 2026

Type: article

Abstract: Optimising propulsion power along a ship’s voyage is critical for energy efficient voyage planning, as it enables the distribution of propulsion settings along a route to minimise fuel consumption while satisfying operational constraints such as target arrival time. This study proposes a structured discrete-to-continuous optimisation framework to enhance propulsion power allocation strategies. Feasible solutions generated by a modified parallel coupling dynamic programming approach serve as prior knowledge (PCDP), and are subsequently refined using constraint-aware Bayesian optimisation (BO). When combined with a smooth exponential penalty function (BO+EP), the Bayesian optimiser embeds convex constraints directly within the optimisation process, improving convergence behaviour. The framework is validated using full-scale data from four voyages of a chemical tanker. Results demonstrate that the proposed method achieves a tenfold reduction in computational cost compared with explicit constraint (EC), while providing up to around 3% additional fuel savings compared with dynamic programming. Overall, the proposed power allocation strategy reduces fuel consumption by up to 9% on average while satisfying arrival time requirements.

Source: Ocean Engineering

DOI: 10.1016/j.oceaneng.2026.125082

Authors: Daniel Vergara, Shanshan Fu, Wengang Mao, Jonas W. Ringsberg, Tsoulakos Nikolaos

Institutions: Chalmers University of Technology, Shanghai Maritime University

Language: English

Topic: Maritime Transport Emissions and Efficiency

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,150