Article: Service anomaly detection in dry bulk terminals: a machine learning approach Journal: International Journal of Shipping and Transport Logistics (IJSTL) 2023 Vol.17 No.3 pp.281 - 302 Abstract: Bulk terminals are complex environments due to a number of variables that affect terminal performance. Although the analysis of big datasets is destined to become an important component of terminal management, previous research has not addressed this issue yet. This paper aims to shed new light on the operation of dry bulk terminals through a two-stage method based on unsupervised machine learning techniques. The first step gives an overview of the terminal's performance, revealing the strongest associations between the variables, while the second calculates an anomaly score for each vessel through an optimised implementation of the isolation forest. As a result, we detect anomalous services which could be directly attributable to the terminal operator. This method can be used to increase transparency in service and assist the terminal operator and ship agents in future contracts. Inderscience Publishers - linking academia, business and industry through research

Title: Service anomaly detection in dry bulk terminals: a machine learning approach

Authors: Iñigo L. Ansorena

Addresses: Universidad Internacional de La Rioja, c/ Avenida de la Paz, 137, Logroño, La Rioja, Spain

Abstract: Bulk terminals are complex environments due to a number of variables that affect terminal performance. Although the analysis of big datasets is destined to become an important component of terminal management, previous research has not addressed this issue yet. This paper aims to shed new light on the operation of dry bulk terminals through a two-stage method based on unsupervised machine learning techniques. The first step gives an overview of the terminal's performance, revealing the strongest associations between the variables, while the second calculates an anomaly score for each vessel through an optimised implementation of the isolation forest. As a result, we detect anomalous services which could be directly attributable to the terminal operator. This method can be used to increase transparency in service and assist the terminal operator and ship agents in future contracts.

Keywords: bulk cargo terminals; terminal performance; machine learning; association discovery; anomaly detection; anomalous service; inefficient service; association rules.

DOI: 10.1504/IJSTL.2023.134736

International Journal of Shipping and Transport Logistics, 2023 Vol.17 No.3, pp.281 - 302

Accepted: 12 Sep 2022
Published online: 09 Nov 2023 *

Title: Service anomaly detection in dry bulk terminals: a machine learning approach

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