Review of the Decision Support Methods Used in Optimizing Ship Hulls towards Improving Energy Efficiency

Abstract

This paper presents a review of the different methods and techniques used to optimize ship hulls over the last six years (2017–2022). This review shows the different percentages of reduction in ship resistance, and thus in the fuel consumption, to improve ships’ energy efficiency, towards achieving the goal of maritime decarbonization. Operational research and machine learning are the common decision support methods and techniques used to find the optimal solution. This paper covers four research areas to improve ship hulls, including hull form, hull structure, hull cleaning and hull lubrication. In each area of research, several computer programs are used, depending on the study’s complexity and objective. It has been found that no specific method is considered the optimum, while the combination of several methods can achieve more accurate results. Most of the research work is focused on the concept stage of ship design, while research on operational conditions has recently taken place, achieving an improvement in energy efficiency. The finding of this study contributes to mapping the scientific knowledge of each technology used in ship hulls, identifying relevant topic areas, and recognizing research gaps and opportunities. It also helps to present holistic approaches in future research, supporting more realistic solutions towards sustainability.