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

Salleh, Nurul Haqimin Mohd; Riahi, Ramin; Yang, Zaili; Wang, Jin

doi:10.1016/j.ajsl.2017.06.007

Cited by 37 publications

(20 citation statements)

References 9 publications

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“…DRM as pre-event decision support includes predicting delay of container vessels. Prediction models using fuzzy rule-based Bayesian network as a hybrid decision technique already exist [4]. A further study presents a prediction model applying data mining and the ML algorithm random forest [10].…”

Section: Literature Review 21 Theoretical Backgroundmentioning

confidence: 99%

“…Besides actual and scheduled shipping data, we collect a large set of explanatory variables. To this end, we conduct a broad literature search followed by an investigation of current shipping reports primarily resulting in port congestion, ports inefficiencies, vessel issues, bad weather, and unreliability of the terminal operator [4]. To verify and challenge this set of variables, we interview six senior experts from leading carriers, academia, and top management consulting firms.…”

Section: Data Collectionmentioning

confidence: 99%

“…Over the last three decades, the container shipping industry has been characterized by significant growth of annual volumes as well as vessel size [2,3]. Congestion at ports and maritime chokepoints such as the Suez Canal, however, represents a major issue causing shipping delay already today [4]. The continuously growing industry is expected to further increase this problem.…”

Section: Introductionmentioning

confidence: 99%

“…The continuously growing industry is expected to further increase this problem. Besides congestion at ports and maritime chokepoints, bad weather also causes delay of container vessels [4]. Extreme weather events often lead to severe disruptions in supply chains, in particular affecting transportation [5].…”

Section: Introductionmentioning

confidence: 99%

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Novel Data Analytics Meets Conventional Container Shipping: Predicting Delays by Comparing Various Machine Learning Algorithms

Viellechner¹,

Spinler²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Supply chain disruptions are expected to significantly increase over the next decades.In particular, delay of container vessels is likely to escalate due to rising congestion from continued growth of container shipping and higher frequency of extreme weather events. Predicting these delays could result in significant cost savings from optimizing operations. Both academic research and container shipping industry, however, lack analytical solutions to predict delay. To increase transparency on delay, we develop a prediction model based on 315 explanatory variables, 10 regression models, and 7 classification models. Using machine learning algorithms, we obtain best results for neural network and support vector machine with a prediction accuracy of 77 percent compared to only 59 percent of a naive baseline model. Various shipping players including sender, carrier, terminal operator, and receiver benefit from the easy-to-use prediction model to optimize operations such as buffers in schedules and the selection of ports and routes.

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Section: Literature Review 21 Theoretical Backgroundmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel Data Analytics Meets Conventional Container Shipping: Predicting Delays by Comparing Various Machine Learning Algorithms

Viellechner¹,

Spinler²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…The cited papers address the search for more accurate methods of ETD prediction; their authors note the complexity of predicting the duration of operations in terminals. Studies [20,21] emphasize the importance of forecasting functions to reduce the costs of port terminals. The ETA and ETD functions are employed by port information systems [22].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Predicting the estimated time of cargo dispatch from a marshaling yard

Panchenko

Prokhorchenko

Panchenko

et al. 2020

EEJET

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6 chain to the destination becomes more complicated. This leads to an increase in the operating costs of a railroad system compared to railroads that follow the system of train traffic on schedule. To improve the competitiveness of a railroad system within which the traffic of trains fails to follow a timetable, it is important to build the functional for a periodic notification about the state of train dispatch. One of the important notification functions is the calculation of the estimated time of arrival (ETA) of a cargo dispatch [2]. Within the framework of the ETA calculation, which is determined for a cargo dispatch at each railroad station 1. Introduction Given the conditions of automation and digitalization of all industries, increasing the level of predictability of the technological process of a rail system transportation is a necessary requirement to reduce the cost of supply chains [1]. The operating model of railroad systems without following the schedule of freight trains possesses a considerable degree of uncertainty in the cargo transportation process. In such systems, the possibility of predicting the duration of operations involving cargo dispatch along a transportation

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