Översikt
- Datum:Startar 27 februari 2025, 09:00Slutar 27 februari 2025, 13:00
- Plats:Lecture Hall HA2, Hörsalsvägen 4
- Opponent:Jin Wang. Professor of Marine Technology, Liverpool John Moores University, United Kingdom.
- AvhandlingLäs avhandlingen (Öppnas i ny flik)
Environmental emissions from shipping pose significant challenges caused by the rapid increase in energy consumption. Voyage optimization system is an valuable tool to address this challenge by enhancing energy efficiency, with optimization algorithms serving as its core, enabling better decision-making. The main objectives of this thesis are to develop voyage optimization algorithms to improve energy efficiency and investigate the capability of voyage optimization algorithms for ship collision avoidance. By achieving these goals, it aims to support intelligent shipping, characterized by enhanced decision-making capabilities. Weather routing, i.e., voyage optimization with the aim to increase energy efficiency in ship operations, rely on ship performance models to estimate energy costs and optimization algorithms to find optimal voyages. However, ship performance models may contain large uncertainties in estimating a ship’s energy consumption and emissions. In addition, optimization algorithms should also consider uncertain and dynamic factors, e.g., weather conditions and market fluctuations, to ensure optimal operations.
To achieve the overall objectives, this thesis first conducts a systematical literature review to help researchers and practitioners clearly understand weather routing and identify opportunities in current research for the development of its optimization algorithms. Based on the review, this thesis proposes two innovative approaches to achieve energy-efficient weather routing, an Isochrone-based predictive optimization algorithm (IPO) and a learning-based multi-objective evolutionary algorithm (L-MOEA). They can effectively minimize fuel consumption and optimize energy efficiency, with the aid of emerging machine learning (ML) techniques. In addition, IPO can be conducted in real-time to address uncertainties in weather routing while considering arrival time, and L-MOEA can consider the essential operational uncertainty due to weather forecast. Furthermore, to ensure reliable operations in practice, this thesis investigates the uncertainty of fuel consumption caused by Specific Fuel Oil Consumption (SFOC) in ship performance models, and the impact of this uncertainty on weather routing. Finally, this thesis extends the research outcome on Isochrone-based algorithms to assist shipping in confined waterways. It seeks to achieve real-time voyage optimization for collision avoidance problems while considering the arrival time, assist on-time transport, and ensure ship operational safety.
It can be concluded that the proposed IPO method can achieve an average of 5% energy savings for weather routing, comparable with L-MOEA. It has also been found that the uncertainty due to ship energy performance models should be carefully considered in decision-making to ensure reliable voyage planning. In addition, the proposed IPO-based collision avoidance algorithm can effectively optimize the voyage in real-time to ensure a ship’s operational safety and on-time arrival, complying with COLREGs in both confined waterways and open waters.
To achieve the overall objectives, this thesis first conducts a systematical literature review to help researchers and practitioners clearly understand weather routing and identify opportunities in current research for the development of its optimization algorithms. Based on the review, this thesis proposes two innovative approaches to achieve energy-efficient weather routing, an Isochrone-based predictive optimization algorithm (IPO) and a learning-based multi-objective evolutionary algorithm (L-MOEA). They can effectively minimize fuel consumption and optimize energy efficiency, with the aid of emerging machine learning (ML) techniques. In addition, IPO can be conducted in real-time to address uncertainties in weather routing while considering arrival time, and L-MOEA can consider the essential operational uncertainty due to weather forecast. Furthermore, to ensure reliable operations in practice, this thesis investigates the uncertainty of fuel consumption caused by Specific Fuel Oil Consumption (SFOC) in ship performance models, and the impact of this uncertainty on weather routing. Finally, this thesis extends the research outcome on Isochrone-based algorithms to assist shipping in confined waterways. It seeks to achieve real-time voyage optimization for collision avoidance problems while considering the arrival time, assist on-time transport, and ensure ship operational safety.
It can be concluded that the proposed IPO method can achieve an average of 5% energy savings for weather routing, comparable with L-MOEA. It has also been found that the uncertainty due to ship energy performance models should be carefully considered in decision-making to ensure reliable voyage planning. In addition, the proposed IPO-based collision avoidance algorithm can effectively optimize the voyage in real-time to ensure a ship’s operational safety and on-time arrival, complying with COLREGs in both confined waterways and open waters.
