Technology Category
Search and Optimization: Mixed Discrete/Continuous SearchPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsConstraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions
🤖 AI Summary
This study addresses the dual challenge of reducing maritime carbon emissions and operational costs by providing a systematic review of methods for estimating and optimizing ship fuel consumption, encompassing physical models, machine learning approaches, and hybrid modeling paradigms. It innovatively introduces the first comprehensive taxonomy of existing techniques and proposes a novel data fusion framework that integrates Automatic Identification System (AIS) data, onboard sensor readings, and meteorological information. To enhance both predictive accuracy and decision transparency, the work incorporates explainable artificial intelligence (XAI) methodologies. The analysis clarifies the respective strengths and limitations of current approaches and identifies critical challenges—including data quality, real-time optimization, and standardization—thereby offering a structured technical roadmap and outlining promising directions for future research in maritime energy efficiency management.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
fuel consumption
maritime transport
estimation
optimization
carbon emissions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid Models
Data Fusion
Explainable AI
Real-time Optimization
Fuel Consumption Estimation
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