To reduce carbon emissions and minimize shipping costs, improving the fuel efficiency of ships is crucial. Various measures are taken to reduce the total fuel consumption of ships, including optimizing vessel parameters and selecting routes with the lowest fuel consumption. Different estimation methods are proposed for predicting fuel consumption, while various optimization methods are proposed to minimize fuel oil consumption. This paper provides a comprehensive review of methods for estimating and optimizing fuel oil consumption in maritime transport. Our novel contributions include categorizing fuel oil consumption \& estimation methods into physics-based, machine-learning, and hybrid models, exploring their strengths and limitations. Furthermore, we highlight the importance of data fusion techniques, which combine AIS, onboard sensors, and meteorological data to enhance accuracy. We make the first attempt to discuss the emerging role of Explainable AI in enhancing model transparency for decision-making. Uniquely, key challenges, including data quality, availability, and the need for real-time optimization, are identified, and future research directions are proposed to address these gaps, with a focus on hybrid models, real-time optimization, and the standardization of datasets.

翻译：为减少碳排放并降低航运成本，提高船舶燃油效率至关重要。为降低船舶总燃油消耗，业界已采取多种措施，包括优化船舶参数和选择燃油消耗最低的航线。针对燃油消耗预测已提出不同的估计方法，同时为最小化燃油消耗亦提出了多种优化方法。本文对海运燃油消耗估计与优化方法进行了全面综述。我们的创新贡献包括将燃油消耗估计方法分为基于物理原理的模型、机器学习模型和混合模型，并探讨了各类方法的优势与局限性。此外，我们强调了数据融合技术的重要性，该技术通过整合AIS数据、船载传感器数据和气象数据来提升估计精度。我们首次尝试探讨可解释人工智能在提升决策模型透明度方面的新兴作用。本文独特地指出了包括数据质量、数据可用性以及实时优化需求在内的关键挑战，并提出了未来研究方向以填补这些空白，重点关注混合模型、实时优化及数据集的标准化。