Titulo Estágio
Smart Berth Planning for commercial ports
Áreas de especialidade
Sistemas Inteligentes
Sistemas de Informação
Local do Estágio
DEI-FCTUC
Enquadramento
The transportation and logistics sector is seeking solutions for a digital transition. There is a need for more digital operations, particularly in the automation of processes and operations, to reduce the need for human interaction. This will contribute to reducing significantly existing inefficiencies. On the other hand, sustainability is also key and has been a trend in the last few years. The sector is being pressed for a green transition, since it is the EU's second-largest source of greenhouse gas (GHG) emissions, with traffic expected to increase around 40% by 2050, according to the European Commission.
As a vital part of the transportation and logistics sector, Ports are no exception. They face several challenges regarding this double transition (i.e., digital and green transition), representing great research and innovation opportunities.
Among these challenges, there is a need for smarter berth planning. This master thesis is intended to research solutions to optimize berth planning, by incorporating predictive analytics to significantly improve decision making.
Objetivo
The main objectives of this thesis are as follows:
- Research and development of predictive models supported by machine learning techniques to drive smarter berth planning in commercial ports
- Integrate such models into a digital twin of a commercial port as a proof-of-concept.
Plano de Trabalhos - Semestre 1
The main phases for the first semester are presented as follows (may overlap):
1. Research the state-of-the-art using a systematic literature review [1,2] on predictive models based on machine learning techniques to support smart berth planning tools and process automation
2. Identify and test existing algorithms that can be incorporated into smart berth planning tools
3. Design the smart berth system taking into account the requirements specified in the previous steps and close connection with stakeholders from the NEXUS project.
Results of the 1st semester:
- Literature review on predictive models for smart berth planning
- Architecture for the berth planning system
- Intermediate report
References:
[1] Webster, J., Watson, R.T.: (2011). Analyzing the Past To Prepare for the Future: Writing a Review. MIS Quartely. 26 (2), 12.
[2] Kitchenham, B., Charters, S.: (2007). Procedures for Performing Systematic Literature Reviews in Software Engineering. Keele Univ. Durham Univ. UK.
Plano de Trabalhos - Semestre 2
The main phases for the second semester are presented as follows:
4. Develop and test a prototype to showcase the smart berth planning system
5. Write the scientific paper and the master thesis
Results of the 2nd semester:
- Berth planning system prototype
- Final report
- Scientific paper: Smart berth planning for commercial ports
Condições
The student will be integrated into CISUC's (Adaptive Computation Group and Information Systems Group) activities, the Department of Informatics Engineering, University of Coimbra.
A workplace and the required resources will be provided.
The supervisors of the thesis will be:
- Prof. Jacinto Estima
- Prof. Alberto Cardoso
Observações
The internship may benefit from a research grant supported by the ongoing NEXUS project.
The internship may benefit from a research grant supported by the ongoing NEXUS project.
The student will use two public datasets. The first dataset consists of AIS data from vessels arriving at Port of Miami. The dataset contains information describing the vessels’ movement, characteristics, and navigational status. The second dataset corresponds to data gathered from a single terrestrial AIS receiver located near Port of Brest.
Additionally, within the scope of the NEXUS project, the Port of Sines will make national data available to explore the specific case of the Port of Sines.
Orientador
Jacinto Estima; Alberto Cardoso
estima@dei.uc.pt 📩