Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times

Elena Villalobos (), Adolfo de Unánue T. (), Fernanda Sobrino (), David Aké (), Stephany Cisneros (), Jorge Lecona and Alejandra Matadamaz
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Elena Villalobos: School of Government and Public Transformation, Tecnológico de Monterrey
Adolfo de Unánue T.: School of Government and Public Transformation, Tecnológico de Monterrey
Fernanda Sobrino: School of Government and Public Transformation, Tecnológico de Monterrey
David Aké: School of Government and Public Transformation, Tecnológico de Monterrey
Stephany Cisneros: School of Government and Public Transformation, Tecnológico de Monterrey
Jorge Lecona: Container Terminal Operations, Veracruz, Mexico
Alejandra Matadamaz: Container Terminal Operations, Veracruz, Mexico

No 31, Working Paper Series of the School of Government and Public Transformation from School of Governement and Public Transformation

Abstract: This article presents the results of a data science study conducted at a container terminal, aimed at reducing unproductive container moves through the prediction of service requirements and container dwell times. We develop and evaluate machine learning models that leverage historical operational data to anticipate which containers will require pre-clearance handling services prior to cargo release and to estimate how long they are expected to remain in the terminal. As part of the data preparation process, we implement a classification system for cargo descriptions and perform deduplication of consignee records to improve data consistency and feature quality. These predictive capabilities provide valuable inputs for strategic planning and resource allocation in yard operations. Across multiple temporal validation periods, the proposed models consistently outperform existing rule-based heuristics and random baselines in precision and recall. These results demonstrate the practical value of predictive analytics for improving operational efficiency and supporting data-driven decision-making in container terminal logistics.

Keywords: machine learning; port terminal; container dwell time; decision support systems; predictive analytics; operational efficiency; logistics; Mexico (search for similar items in EconPapers)
JEL-codes: C53 C55 L91 R41 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2026-04
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https://egobiernoytp.tec.mx/sites/default/files/20 ... _and_dwell_times.pdf First version, 2026 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:gnt:wpaper:31

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