A data mining approach to forecast late arrivals in a transhipment container terminal

AbstractOne of the most important issues in Transhipment Container Terminal (TCT) management is to have fairly reliable and affordable predictions about vessel arrival. Terminal operators need to estimate the actual time of arrival in port in order to determine the daily demand for each work shift with greater accuracy. In this way, the resources required (human resources, equipment as well as spatial resources) can be allocated more efficiently. Despite contractual obligations to notify the Estimated Time of Arrival (ETA) 24 hours before arrival, ship operators often have to revise it due to unexpected events like weather conditions, delay in a previous port and so on. For planners the decision-making processes related to this topic can sometimes be so complex without the support of suitable methodological tools. Specific models should be adopted, in a daily planning scenario, to provide a useful support tool in TCTs. In this study, we discuss an exploratory analysis of the data affecting delays register...

[1]  W. Loh,et al.  LOTUS: An Algorithm for Building Accurate and Comprehensible Logistic Regression Trees , 2004 .

[2]  J. Morgan,et al.  Thaid a Sequential Analysis Program for the Analysis of Nominal Scale Dependent Variables , 1973 .

[3]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[4]  Loo Hay Lee,et al.  A decision model for berth allocation under uncertainty , 2011, Eur. J. Oper. Res..

[5]  K. H. Kim,et al.  An integrated framework for various operation plans in container terminals , 2009 .

[6]  Rakesh Agrawal,et al.  SPRINT: A Scalable Parallel Classifier for Data Mining , 1996, VLDB.

[7]  Richard G. Mathieu,et al.  Kanban setting through artificial intelligence: a comparative study of artificial neural networks and decision trees , 2000 .

[8]  K. Hornik,et al.  Unbiased Recursive Partitioning: A Conditional Inference Framework , 2006 .

[9]  Luca Maria Gambardella,et al.  Simulation and Planning of an Intermodal Container Terminal , 1998, Simul..

[10]  G. V. Kass.  An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .

[11]  Donald E. Brown,et al.  A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems , 1992, Pattern Recognit..

[12]  Claudia Pani,et al.  Prediction of arrival times and human resources allocation for container terminal , 2011 .

[13]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[14]  Luca Maria Gambardella,et al.  Simulation and Forecasting in an Intermodal Container Terminal , 1996 .

[15]  W. Loh,et al.  Tree-Structured Classification via Generalized Discriminant Analysis. , 1988 .

[16]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[17]  Roberta Siciliano,et al.  A fast splitting procedure for classification trees , 1997, Stat. Comput..

[18]  Mario Rodríguez-Molins,et al.  A decision support system for managing combinatorial problems in container terminals , 2012, Knowl. Based Syst..

[19]  M. Fung,et al.  Forecasting Hong Kong's container throughput: an error-correction model , 2002 .

[20]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[21]  Stefan Voß,et al.  Operations research at container terminals: a literature update , 2007, OR Spectr..

[22]  Jorma Rissanen,et al.  SLIQ: A Fast Scalable Classifier for Data Mining , 1996, EDBT.

[23]  Claudio Conversano,et al.  Bagged Mixtures of Classifiers using Model Scoring Criteria , 2002, Pattern Analysis and Applications.

[24]  Katta G. Murty,et al.  A decision support system for operations in a container terminal , 2005, Decis. Support Syst..

[25]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[26]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[27]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[28]  J. Morgan,et al.  Problems in the Analysis of Survey Data, and a Proposal , 1963 .

[29]  Padhraic Smyth,et al.  Knowledge Discovery and Data Mining: Towards a Unifying Framework , 1996, KDD.

[30]  James J. Chen,et al.  Regression trees for analysis of count data with extra Poisson variation , 2005, Comput. Stat. Data Anal..

[31]  Iris F. A. Vis,et al.  Transshipment of containers at a container terminal: An overview , 2003, Eur. J. Oper. Res..

[32]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[33]  Philip J. Stone,et al.  Experiments in induction , 1966 .

[34]  Gregory Piatetsky-Shapiro,et al.  Knowledge Discovery in Databases: An Overview , 1992, AI Mag..

[35]  Xiaogang Su,et al.  Joint Statistical Meetings- Statistical Computing Section Maximum Likelihood Regression Trees , 2022 .