Data analytics for fuel consumption management in maritime transportation: Status and perspectives

• Thoroughly review and compare 83 papers on vessel fuel consumption prediction optimization in recent 13 years. Present features sources of main datasets used current ship models. Classify models into white-box (WBMs), black-box (BBMs), grey-box (GBMs) combining WBMs BBMs. Analyze from the perspectives decision variables objective functions. Review technical reports as a typical type “grey literature”. Propose discuss research challenges promising questions management. The shipping industry is associated with approximately three quarters all world trade. In years, sustainability has become public concern, various emissions control regulations to reduce pollutants greenhouse gas (GHG) ships have been proposed implemented globally. These aim drive low-carbon low-pollutant direction by motivating it switch more efficient types energy consumption. At same time, cyclical downturn economy high bunker prices make necessary urgent for operate cost-effective way while still satisfying global trade demand. As (e.g., heavy oil [HFO], liquified natural [LNG]) source costs account large proportion operating costs, companies are making unprecedented efforts optimize efficiency. It widely accepted that key improving efficiency development accurate predict rates under different scenarios. this study, presented literature (including academic literature”) reviewed compared, operations based results also discussed. Current performance monitoring operational identified.