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Automatic identification system (AIS) data based ship-supply forecasting

In: Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 28

Author

Listed:
  • Lechtenberg, Sandra
  • de Siqueira Braga, Diego
  • Hellingrath, Bernd

Abstract

Purpose: The bulk cargo shipping industry is characterized by high cost pressure. Chartering vessels at low prices is important to increase the margin of transporting cargo. This paper proposes a three-step, AI-based methodology to support this by forecasting the number of available ships in a region at a certain time. Methodology: Resulting from discussions with experts, this work proposes a threestep process to forecast ship numbers. It implements, compares and evaluates different AI approaches for each step based on sample AIS data: Markov decision process, extreme gradient boosting, artificial neural network and support vector machine. Findings: Forecasting ship numbers is done in three steps: Predicting the (1) next unknown destination, (2) estimated time of arrival and (3) anchor time for each ship. The proposed prediction approach utilizes Markov decision processes for step (1) and extreme gradient boosting for step (2) and (3). Originality: The paper proposes a novel method to forecast the number of ships in a certain region. It predicts the anchor time of each ship with an MAE of 5 days and therefore gives a good estimation, i.e. the results of this method can support ship operators in their decision-making.

Suggested Citation

  • Lechtenberg, Sandra & de Siqueira Braga, Diego & Hellingrath, Bernd, 2019. "Automatic identification system (AIS) data based ship-supply forecasting," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 3-24, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
  • Handle: RePEc:zbw:hiclch:209386
    DOI: 10.15480/882.2487
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    References listed on IDEAS

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    1. Carbonneau, Real & Laframboise, Kevin & Vahidov, Rustam, 2008. "Application of machine learning techniques for supply chain demand forecasting," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1140-1154, February.
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    Cited by:

    1. Kei Kanamoto & Liwen Murong & Minato Nakashima & Ryuichi Shibasaki, 2021. "Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 211-236, June.
    2. Weinke, Manuel & Poschmann, Peter & Straube, Frank, 2021. "Decision-making in multimodal supply chains using machine learning," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 301-325, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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