I³-Lab Business Analytics – Optimisation Potentials and Strategic Risks for Maritime Logistics Systems
Within the framework of the project, institutes from different fields work together on questions of business analytics in maritime logistics. The project is thus at the interface of computer science, mathematics, management and logistics and is therefore highly interdisciplinary.
Project duration | 01.08.2018 – 15.10.2022 |
Project funding | funded by Administration for Science, Research and Equality Hamburg |
Our status | Project partner |
Contact person | Marvin Kastner |
Project homepage | https://www2.tuhh.de/i3-ba-ml |
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Description
The rapidly increasing amount of available and usable data and the recent increased performance of existing computers enables data analyses and calculations on a scale that was unthinkable just a few years ago. While at present, the immense performance of algorithms is often uncritically accepted, but possible risks are often completely ignored. This opens up new challenges for university teaching and research. Along with digitalization, companies also want and need to adapt corresponding processes and they need new research results in order to implement methods of business analytics in the form of innovative solutions.
The project is mainly dedicated to the application of business analytics in the field of maritime logistic systems, as there is still great potential for optimization. On the other hand, this industry now has huge amounts of data, such as ship movements and weather data. The evaluation of which can enable the development of improved strategies in personnel and fleet deployment or revenue management, and new solutions, for example in autonomously controlled ship traffic.
Publications (Excerpt)
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
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on pages: 70-78 |
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Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
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ISBN: 978-3-030-44782-3 |
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DOI: 10.1007/978-3-030-44783-0_7 |
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Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times