Sustainable Maritime Transportation Network considering Sulphur Fuel Regulation - Application of Advanced Machine Learning and Optimization
Lead Research Organisation:
University of Manchester
Department Name: Social Sciences
Abstract
The International Maritime Organization (IMO) highlighted immediate actions to mitigate carbon emission growth, a major factor in climate change. The United Nations Conference on Trade and Development has stated that international shipping carries 80% of world trade making a significant contribution to the rise in carbon dioxide emissions.
Zis et al. (2019) highlighted that in 2015, many vessels used bunker fuel oil, which contributes 3.5% to global Sulphur oxides emissions, thereby significantly increasing the environmental and health problems (heart and lung diseases) around populated coastal areas. Rising atmospheric concentrations of carbon dioxide and sulphur oxides are causing oceans to absorb more of the gases and become more acidic leading to a significant impact on coastal and marine ecosystems.
Consequently, IMO 2020 adopted strict regulations for emission control areas (ECAs), where ships must use fuel oil with a Sulphur content of no more than 0.1%. Furthermore, for mitigating the emission from maritime logistics, existing literature has highlighted the adoption of various measures such as carbon taxes, slow steaming policy and bunkering strategies. Adoption of such measures depends on fuel prices and a major issue for shipping companies is the fluctuation in fuel prices at (and between) ports.
Existing literature on maritime logistics that focuses on predicting fuel prices is still at nascent stage, and there is a gap in the area of developing advanced machine learning algorithms that predict bunker fuel prices. This project will involve working with the partner company, to develop a machine learning model for fuel prices prediction at port using a dataset containing data on CO2 emissions and bunker fuel prices. Past literature has overlooked the IMO 2020 regulations related to the use of low-sulphur fuel oil for bunkering purpose. Therefore, the current project would facilitate bunkering decisions (i.e. choosing the refuelling port and determining the refuelling amount) of the shipping companies considering the IMO regulations by developing a multi-objective optimization model (mixed integer linear programming model) for minimizing the bunkering cost and emissions. Several authors have highlighted the need for considering slow steaming policy (or, speed optimization) and accurate fuel price information at the ports for adequately perform the bunker fuel management. Therefore, the current research project aims to consider bunker price information obtained from the machine learning model and integrate it within the multi-objective optimization model for determining the bunkering strategies while minimizing the carbon and sulphur emissions. The following are indicative research questions:
1. Can we develop a reliable predictive model for estimating bunker fuel prices and CO2 emissions at ports?
2. Can we propose a holistic formal multi-objective optimization model (mixed integer linear programming model) to tune maritime transportation networks? Such a model would need to comprise several objectives including ones related to sustainability, costs, and emissions, while capturing sensible constraints and decision variables to be tuned.
3. How can we integrate the insights pertaining to bunker price information obtained from the predictive model with the optimisation model for determining the bunkering strategies while facilitating sulphur and carbon emission reduction within the maritime transportation network?
4. What would be robust predictive models and multi-objective models to optimize the problem?
Zis et al. (2019) highlighted that in 2015, many vessels used bunker fuel oil, which contributes 3.5% to global Sulphur oxides emissions, thereby significantly increasing the environmental and health problems (heart and lung diseases) around populated coastal areas. Rising atmospheric concentrations of carbon dioxide and sulphur oxides are causing oceans to absorb more of the gases and become more acidic leading to a significant impact on coastal and marine ecosystems.
Consequently, IMO 2020 adopted strict regulations for emission control areas (ECAs), where ships must use fuel oil with a Sulphur content of no more than 0.1%. Furthermore, for mitigating the emission from maritime logistics, existing literature has highlighted the adoption of various measures such as carbon taxes, slow steaming policy and bunkering strategies. Adoption of such measures depends on fuel prices and a major issue for shipping companies is the fluctuation in fuel prices at (and between) ports.
Existing literature on maritime logistics that focuses on predicting fuel prices is still at nascent stage, and there is a gap in the area of developing advanced machine learning algorithms that predict bunker fuel prices. This project will involve working with the partner company, to develop a machine learning model for fuel prices prediction at port using a dataset containing data on CO2 emissions and bunker fuel prices. Past literature has overlooked the IMO 2020 regulations related to the use of low-sulphur fuel oil for bunkering purpose. Therefore, the current project would facilitate bunkering decisions (i.e. choosing the refuelling port and determining the refuelling amount) of the shipping companies considering the IMO regulations by developing a multi-objective optimization model (mixed integer linear programming model) for minimizing the bunkering cost and emissions. Several authors have highlighted the need for considering slow steaming policy (or, speed optimization) and accurate fuel price information at the ports for adequately perform the bunker fuel management. Therefore, the current research project aims to consider bunker price information obtained from the machine learning model and integrate it within the multi-objective optimization model for determining the bunkering strategies while minimizing the carbon and sulphur emissions. The following are indicative research questions:
1. Can we develop a reliable predictive model for estimating bunker fuel prices and CO2 emissions at ports?
2. Can we propose a holistic formal multi-objective optimization model (mixed integer linear programming model) to tune maritime transportation networks? Such a model would need to comprise several objectives including ones related to sustainability, costs, and emissions, while capturing sensible constraints and decision variables to be tuned.
3. How can we integrate the insights pertaining to bunker price information obtained from the predictive model with the optimisation model for determining the bunkering strategies while facilitating sulphur and carbon emission reduction within the maritime transportation network?
4. What would be robust predictive models and multi-objective models to optimize the problem?
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ES/T002085/1 | 01/10/2020 | 30/09/2027 | |||
| 2885828 | Studentship | ES/T002085/1 | 01/10/2023 | 30/09/2027 | Qian Zhao |