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Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN)

Salleh, NHM, Riahi, R, Yang, Z and Wang, J (2017) Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN). Asian Journal of Shipping and Logistics, 33 (2). pp. 95-104. ISSN 2092-5212

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Abstract

One of the biggest concerns in liner operations is punctuality of containerships. Managing the time factor has become a crucial issue in today's liner shipping operations. A statistic in 2015 showed that the overall punctuality for containerships only reached an on-time performance of 73%. However, vessel punctuality is affected by many factors such as the port and vessel conditions and knock-on effects of delays. As a result, this paper develops a model for analyzing and predicting the arrival punctuality of a liner vessel at ports of call under uncertain environments by using a hybrid decision-making technique, the Fuzzy Rule-Based Bayesian Network (FRBBN). In order to ensure the practicability of the model, two container vessels have been tested by using the proposed model. The results have shown that the differences between prediction values and real arrival times are only 4.2% and 6.6%, which can be considered as reasonable. This model is capable of helping liner shipping operators (LSOs) to predict the arrival punctuality of their vessel at a particular port of call. © 2017 The Korean Association of Shipping and Logistics, Inc.

Item Type: Article
Subjects: H Social Sciences > HE Transportation and Communications
Divisions: Maritime and Mechanical Engineering
Publisher: Elsevier
Date Deposited: 15 Mar 2018 11:09
Last Modified: 15 Mar 2018 11:09
DOI or Identification number: 10.1016/j.ajsl.2017.06.007
URI: http://researchonline.ljmu.ac.uk/id/eprint/8306

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