A Data Mining Approach to Forecast Late Arrivals in a Transhipment Container Terminal

Pani, Claudia; Fadda, Paolo; Fancello, Gianfranco; Frigau, Luca; Molà, Francesco

doi:10.3846/16484142.2014.930714

Cited by 39 publications

(14 citation statements)

References 26 publications

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“…The container terminal management problems are studied with the help of machine learning techniques. Pani et al (2014) aimed to forecast the late arrivals in a container terminal. The authors discussed an analysis of the data and presented preliminary results with data mining techniques.…”

Section: Literaturementioning

confidence: 99%

Analysing the Effects of Weather Conditions on Container Terminal Operations Using Machine Learning

Atak

Kaya

Arslanoğlu

2022

International Journal of Business Analytics

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Container ships transport a large number of valuable cargoes, and there is a demand for less expensive and faster transportation options. Weather, vessel type, and the nature and amount of the goods are all external elements that might impact container handling times, which are directly related to overall port stay time. In this scope, container terminal operations could be optimised with the help of historical data which provides access to classification and prediction of the cargo handling operations. In this study, the real-time data of a container terminal operation is analysed with different machine learning techniques along with the Fuzzy C-Means clustering method. The results show that Fuzzy C-Means clustering has a positive impact on the explanatory power of models in container terminal operations. The research revealed that an increase in wind speed influences cargo handling time for mobile cranes.

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Section: Literaturementioning

confidence: 99%

Analysing the Effects of Weather Conditions on Container Terminal Operations Using Machine Learning

Atak

Kaya

Arslanoğlu

2022

International Journal of Business Analytics

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“…orben and Till investigated how a network model with a stochastic block of interconnections was applied to model and predict material flows in manufacturing systems [41]. In particular, Pani et al presented some preliminary results obtained using data mining and proposed a classification and regression trees model to reduce the range of uncertainty of ship arrivals in port in the past [42]. Recently, Gao et al trained the LSTM RNN to predict daily volumes of containers, which entered the storage yard, by deep learning the historical dataset [43].…”

Section: Literature Review and Related Workmentioning

confidence: 99%

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

2021

Discrete Dynamics in Nature and Society

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The synergy of computational logistics and deep learning provides a new methodology and solution to the operational decisions of container terminal handling systems (CTHS) at the strategic, tactical, and executive levels. Above all, the container terminal logistics tactical operational complexity is discussed by computational logistics, and the liner handling volume (LHV) has important influences on a series of terminal scheduling decision problems. Subsequently, a feature-extraction-based lightweight convolutional and recurrent neural network adaptive computing model (FEB-LCR-ACM) is presented initially to predict the LHV by the fusion of multiple deep learning algorithms and mechanisms, especially for the specific feature extraction package of tsfresh. Consequently, the container-terminal-oriented logistics service scheduling decision support design paradigm is put forward tentatively by FEB-LCR-ACM. Finally, a typical large-scale container terminal of China is chosen to implement, execute, and evaluate the FEB-LCR-ACM based on the terminal running log around the indicator of LHV. In the case of severe vibration of LHV between 2 twenty-foot equivalent units (TEUs) and 4215 TEUs, while forecasting the LHV of 300 liners by the log of five years, the forecasting error within 100 TEUs almost accounts for 80%. When predicting the operation of 350 ships by the log of six years, the forecasting deviation within 100 TEUs reaches up to nearly 90%. The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.

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“…Neural network as another ML algorithm is implemented for predicting delay to forecast required human resources more accurately for covering daily port operations [12]. Similarly, more efficient allocation of human resources at ports is approached by a data mining research that suggests a classification and regression tree (CART) model [13]. All these studies regarding DRM, however, limit improvements to terminal operations as they consider the operational planning level.…”

Section: Literature Review 21 Theoretical Backgroundmentioning

confidence: 99%

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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A Data Mining Approach to Forecast Late Arrivals in a Transhipment Container Terminal

Cited by 39 publications

References 26 publications

Analysing the Effects of Weather Conditions on Container Terminal Operations Using Machine Learning

Analysing the Effects of Weather Conditions on Container Terminal Operations Using Machine Learning

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

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

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