Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN)
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%. Howe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017-07-01
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| Series: | Asian Journal of Shipping and Logistics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2092521217300251 |
| _version_ | 1811308185684803584 |
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| author | Nurul Haqimin Mohd Salleh Ramin Riahi Zaili Yang Jin Wang |
| author_facet | Nurul Haqimin Mohd Salleh Ramin Riahi Zaili Yang Jin Wang |
| author_sort | Nurul Haqimin Mohd Salleh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T09:17:39Z |
| format | Article |
| id | doaj.art-5a4d6a2278a94143a8b2d451d47d2f48 |
| institution | Directory Open Access Journal |
| issn | 2092-5212 |
| language | English |
| last_indexed | 2024-04-13T09:17:39Z |
| publishDate | 2017-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Asian Journal of Shipping and Logistics |
| spelling | doaj.art-5a4d6a2278a94143a8b2d451d47d2f482022-12-22T02:52:41ZengElsevierAsian Journal of Shipping and Logistics2092-52122017-07-013329510410.1016/j.ajsl.2017.06.007Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN)Nurul Haqimin Mohd Salleh0Ramin Riahi1Zaili Yang2Jin Wang3Senior Lecturer, Universiti Malaysia Terengganu, MalaysiaShip Superintendent, Columbia Ship Management (Deutschland), GermanyProfessor, Liverpool John Moores UniversityProfessor, Liverpool John Moores UniversityOne 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.http://www.sciencedirect.com/science/article/pii/S2092521217300251Schedule ReliabilityArrival PunctualityLiner ShippingFuzzy Rule-Based Bayesian Reasoning |
| spellingShingle | Nurul Haqimin Mohd Salleh Ramin Riahi Zaili Yang Jin Wang Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN) Asian Journal of Shipping and Logistics Schedule Reliability Arrival Punctuality Liner Shipping Fuzzy Rule-Based Bayesian Reasoning |
| title | Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN) |
| title_full | Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN) |
| title_fullStr | Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN) |
| title_full_unstemmed | Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN) |
| title_short | Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN) |
| title_sort | predicting a containership s arrival punctuality in liner operations by using a fuzzy rule based bayesian network frbbn |
| topic | Schedule Reliability Arrival Punctuality Liner Shipping Fuzzy Rule-Based Bayesian Reasoning |
| url | http://www.sciencedirect.com/science/article/pii/S2092521217300251 |
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