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Automatic identification system (AIS) data based ship-supply forecasting
Citation Link: https://doi.org/10.15480/882.2487
Publikationstyp
Conference Paper
Publikationsdatum
2019-09-26
Sprache
English
TORE-URI
First published in
Number in series
28
Start Page
3
End Page
24
Citation
Hamburg International Conference of Logistics (HICL) 28: 3-24 (2019)
Contribution to Conference
Publisher
epubli GmbH
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.
Schlagworte
AIS data
Ship-supply forecasting
Dry bulk cargo
Artificial intelligence
DDC Class
004: Informatik
330: Wirtschaft
380: Handel, Kommunikation, Verkehr
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Lechtenberg_Braga_Hellingrath-Automatic_Identification_System_(AIS)_data_based_Ship-Supply_Forecasting_hicl_2019.pdf
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Format
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