Sustainable Maritime Transportation Network considering Sulphur Fuel Regulation – Application of Advanced Machine Learning and Optimization

The International Maritime Organization (IMO) highlighted immediate actions to mitigate carbon emission growth, a major factor in climate change (Wang et al. 2021). 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 (UNCTAD 2019). 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% (Wang et al. 2021). 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 (Zhen et al. 2017). 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 (Zhen et al. 2017).

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 (MaritimeAPI), 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 (Wang et al. 2021). 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 (Aydin et al. 2017, De et al. 2021). 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

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?

The project is in collaboration with two organizations – Port of Dover and MaritimeAPI Ltd. The companies would support the project in terms of sharing fuel price data which would be required for model development for forecasting fuel prices. Furthermore, carbon emission data would be received from the companies to estimate the carbon and sulphur footprint at ports. The organizations would be involved in guiding the research project to meet the aims and objectives related to sustainability, cost and emission.

The academic supervisory team includes Arijit De and Richard Allmendinger (both Alliance Manchester Business School). For queries regarding the project, please email the supervisors directly ([Email Address Removed], [Email Address Removed]).

Entry requirements

2:1 honours degree, or equivalent, in a relevant subject area

English language requirements (for international/EU candidates)

You have must have one or more of the following:

• IELTS (International English Language Testing System) – 7 overall, 7 writing, 6 other sections
• TOEFL (Test of English as a Foreign Language) Internet based test – 100 overall, 25 writing, 22 other sections
• A degree in any subject completed in the English
• language from a majority English speaking country may also be acceptable –please check here https://www.leeds.ac.uk/admissions-qualifications for the country where you completed your degree.

How to Apply:

To apply please complete an online application form. Please note – you must apply through the Leeds portal, even if you wish to study at Manchester. Do not apply directly to Manchester.

Supporting Documents: Please visit https://datacdt.org/entry-criteria-applying/ to ensure you include the required documents with your application form.

We have funds available to help with accessibility and widening participation, please get in touch for details.