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
Project partners
  • TUHH Institute of Operations Research and Information Systems
    Prof. Dr. Kathrin Fischer
  • TUHH Institute of Maritime Logistics
    Prof. Dr.-Ing. Carlos Jahn
  • TUHH Institute of Mathematics
    Prof. Dr. Anusch Taraz
  • TUHH Institute of Strategical und International Management
    Prof. Dr. Thomas Wrona

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)

  • Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin (2022). Robust berth scheduling using machine learning for vessel arrival time prediction. Flexible Services and Manufacturing Journal. 35. 29-69 [Abstract] [pdf] [doi] [www]

  • Franzkeit, Janna and Pache, Hannah and Jahn, Carlos (2020). Investigation of Vessel Waiting Times Using AIS Data. In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) Dynamics in Logistics Springer International Publishing: Cham 70-78 [Abstract] [doi]

  • Pache, Hannah and Grafelmann, Michaela and Schwientek, Anne Kathrina and Jahn, Carlos (2020). Tactical planning in tramp shipping – a literature review. In Jahn, Carlos and Kersten, Wolfgang and Ringle, Christian M. (Eds.) Data science and innovation in supply chain management : how data transforms the value chain // Proceedings of the Hamburg International Conference of Logistics (HICL)/ Data Science in Maritime and City Logistics epubli: Berlin 282-308 [Abstract] [doi]

  • Pache, Hannah and Kastner, Marvin and Jahn, Carlos (2019). Current state and trends in tramp ship routing and scheduling. In Jahn, Carlos and Kersten, Wolfgang and Ringle, Christian M. (Eds.) Digital transformation in maritime and city logistics epubli: 369-394 [Abstract] [doi] [www]