arxiv: 2604.06251 · v1 · submitted 2026-04-06 · 💻 cs.AI · cs.LG· stat.AP

Recognition: no theorem link

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

Elena Villalobos (1) , Adolfo De Un\'anue T. (1) , Fernanda Sobrino (1) , David Ak\'e (1) , Stephany Cisneros (1) , Jorge Lecona (2) , Alejandra Matadamaz (2) ((1) Tecnol\'ogico de Monterrey , Mexico City

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Mexico (2) Container Terminal Operations Veracruz Mexico)

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:25 UTC · model grok-4.3

classification 💻 cs.AI cs.LGstat.AP

keywords container terminalmachine learningdwell time predictionservice requirement predictionunproductive movesoperational efficiencypredictive analyticsyard operations

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The pith

Machine learning models trained on terminal data can predict which containers need pre-clearance services and how long they will stay.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops and tests machine learning models that use historical operational records to forecast two things at a container terminal: whether a container will require pre-clearance handling before release, and how long it will remain on site. Data preparation includes classifying cargo descriptions and removing duplicate consignee entries to improve feature quality. The models are evaluated over multiple future time periods and show higher precision and recall than both existing rule-based methods and random guessing. If the forecasts hold, they supply advance information that lets yard managers schedule moves and allocate equipment more efficiently, cutting the number of unproductive container relocations.

Core claim

Machine learning models that leverage historical operational data, after cargo-description classification and consignee deduplication, can anticipate service requirements and dwell times for containers and thereby provide inputs for yard planning; across several temporal validation windows these models achieve higher precision and recall than rule-based heuristics or random baselines.

What carries the argument

Machine learning models that predict pre-clearance service needs and container dwell times from cleaned historical terminal records.

If this is right

Yard operations can use the forecasts to allocate equipment and labor before containers arrive.
Fewer unproductive moves follow from advance knowledge of which containers require extra handling.
Resource planning becomes data-driven rather than reactive.
The same cleaned data pipeline can support additional predictive tasks at the terminal.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The approach could be extended to predict optimal storage locations inside the yard rather than only dwell time.
Terminals with different equipment or cargo profiles would need fresh training data to maintain the reported accuracy.
Real-time updates to the models as new containers enter the gate could improve short-horizon forecasts.

Load-bearing premise

Historical operational data from the terminal will continue to reflect future conditions without major shifts in cargo types, regulations, or procedures.

What would settle it

Applying the trained models to a later data set collected after a documented change in cargo mix or terminal rules and finding that precision and recall fall to the level of the rule-based baselines would falsify the claim.

Figures

Figures reproduced from arXiv: 2604.06251 by (2) Container Terminal Operations, Adolfo De Un\'anue T. (1), Alejandra Matadamaz (2) ((1) Tecnol\'ogico de Monterrey, David Ak\'e (1), Elena Villalobos (1), Fernanda Sobrino (1), Jorge Lecona (2), Mexico, Mexico), Mexico City, Stephany Cisneros (1), Veracruz.

**Figure 1.** Figure 1: Data product pipeline. This graph-based consolidation process results in a reduced and more consistent consignee catalog, where each connected component represents a unique underlying entity. By resolving duplicate identities and harmonizing consignee information, this procedure improves data consistency and reduces noise in downstream analyses. The resulting consolidated consignee identifier is subsequent… view at source ↗

**Figure 2.** Figure 2: Weekly average precision and recall for the service label across the main evaluated models. The upper panel [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

**Figure 3.** Figure 3: Precision and recall for different k values under varying service area capacities [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

**Figure 4.** Figure 4: Weekly average precision and recall for dwell times of less than two days. [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗

**Figure 5.** Figure 5: Weekly average precision and recall for containers with a dwell time of exactly five days. This label is used as [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗

**Figure 6.** Figure 6: Weekly average precision and recall for dwell times longer than seven days. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗

**Figure 7.** Figure 7: Average precision and recall of the best-performing models across dwell-time labels. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 8.** Figure 8: Correct and incorrect classifications by dwell-time category on a typical operational day. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗

**Figure 9.** Figure 9: Jaccard similarity matrix across dwell-time labels. [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper shows standard ML can beat rules for predicting container services and dwell times at one terminal, with temporal validation as the main strength but thin detail on features and timing.

read the letter

The one or two things to know: this is an applied study showing that machine learning models can outperform basic rule-based methods when predicting service requirements and dwell times for containers at a specific terminal. The work comes from researchers at Tecnológico de Monterrey working with the Veracruz terminal operators. They do a good job with the data preparation, using a classification system for cargo descriptions and deduplicating consignee records to clean up the historical data. Then they evaluate the models with temporal validation periods, which is the right way to check if it would work prospectively. The claim is that precision and recall improve over heuristics and random baselines, which seems plausible for this kind of operational data. The soft spots are mostly around transparency. The description doesn't include the list of features, the model architectures, or how they handled things like class imbalance. Without those, it's difficult to replicate or fully evaluate if the improvements are robust. There's also the question of when exactly the predictions would be made in practice—does the model only use data available at the time of booking, or does it rely on anything that happens later? The temporal split helps, but more explicit confirmation would strengthen it. Overall, this paper is for readers interested in real applications of predictive analytics in logistics and port operations. It doesn't introduce new algorithms or theory, but it demonstrates value in a concrete setting with appropriate validation. I'd say send it to peer review. The practical contribution and the temporal evaluation make it worth a closer look from referees who can probe the details.

Referee Report

2 major / 2 minor

Summary. The paper develops and evaluates machine learning models that use historical operational data from a container terminal to predict which containers will require pre-clearance handling services and to estimate their dwell times. As part of data preparation, the authors implement cargo classification and consignee deduplication. The central claim is that these models, evaluated across multiple temporal validation periods, consistently outperform rule-based heuristics and random baselines in precision and recall, thereby providing inputs for reducing unproductive container moves and improving yard operations.

Significance. If the outperformance holds under strictly causal conditions with no data leakage, the work offers practical value for data-driven resource allocation in container terminal logistics. The explicit use of temporal validation periods is a methodological strength that avoids obvious leakage from future information, and the focus on operational metrics like precision and recall aligns with real-world decision needs.

major comments (2)

[Abstract] Abstract and Methods: The outperformance claim in precision and recall is load-bearing for the paper's contribution, yet the abstract supplies no feature list, no description of the exact time at which predictions are issued (e.g., at booking versus arrival), and no explicit statement confirming that all validation splits are strictly causal (future periods use only information available at the decision point). Without these details, it is impossible to verify that the reported gains over heuristics would replicate in live deployment.
[Validation] Validation setup: The temporal validation is described only at a high level; the manuscript must specify the exact number of periods, the length of each hold-out window, how class imbalance was handled during training and evaluation, and whether any post-event features (e.g., actual release dates) inadvertently entered the feature set. These omissions directly affect the soundness of the central claim.

minor comments (2)

[Abstract] The abstract mentions 'multiple temporal validation periods' but does not report the actual precision/recall values or the magnitude of improvement over baselines; adding these numbers would improve readability.
[Introduction] Clarify the precise definition of 'unproductive container moves' and how the predicted service requirements and dwell times are intended to be used operationally (e.g., as inputs to a scheduling algorithm).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback emphasizing the need for greater transparency in the abstract and validation methodology. These points are important for demonstrating the practical deployability of our models. We address each major comment below and will revise the manuscript to incorporate the requested clarifications.

read point-by-point responses

Referee: [Abstract] Abstract and Methods: The outperformance claim in precision and recall is load-bearing for the paper's contribution, yet the abstract supplies no feature list, no description of the exact time at which predictions are issued (e.g., at booking versus arrival), and no explicit statement confirming that all validation splits are strictly causal (future periods use only information available at the decision point). Without these details, it is impossible to verify that the reported gains over heuristics would replicate in live deployment.

Authors: We agree that the abstract would benefit from additional context to support the central claims. In the revised version, we will expand the abstract to summarize the primary features (historical operational data, cargo classification, and consignee deduplication), clarify that predictions are issued at the point of booking or terminal arrival using only data available at that decision time, and include an explicit statement confirming that all temporal validation splits are strictly causal with no future information leakage. These additions will be concise and will not alter the reported results. revision: yes
Referee: [Validation] Validation setup: The temporal validation is described only at a high level; the manuscript must specify the exact number of periods, the length of each hold-out window, how class imbalance was handled during training and evaluation, and whether any post-event features (e.g., actual release dates) inadvertently entered the feature set. These omissions directly affect the soundness of the central claim.

Authors: We acknowledge that the validation description is currently high-level and will revise the methods section to supply the missing specifics. The updated text will state the exact number of temporal periods, the duration of each hold-out window, the approach to class imbalance (e.g., class-weighted training and appropriate evaluation metrics), and a detailed account of feature construction confirming that only pre-decision information is used with no post-event features included. This will directly address concerns about causality and replicability. revision: yes

Circularity Check

0 steps flagged

No circularity: standard supervised ML with temporal hold-out evaluation

full rationale

The paper trains ML models on historical terminal data to predict service requirements and dwell times, then reports empirical outperformance on multiple future temporal validation periods against heuristics and random baselines. No equations, fitted parameters, or self-citations are presented that would make any reported prediction equivalent to its inputs by construction. Preprocessing steps (cargo classification, consignee deduplication) are standard feature engineering and do not redefine the target variables. The evaluation protocol described is the conventional non-circular approach for time-series forecasting tasks.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard supervised-learning assumptions plus the domain premise that past terminal records generalize forward in time.

free parameters (1)

model hyperparameters
Hyperparameters for the machine learning models are chosen or tuned on the training data.

axioms (2)

domain assumption Historical terminal records are representative of future operations
Invoked to justify temporal validation and generalization of the learned predictors.
domain assumption Cargo description strings and consignee records can be reliably classified and deduplicated without introducing systematic bias
Stated as part of the data-preparation process that improves feature quality.

pith-pipeline@v0.9.0 · 5515 in / 1133 out tokens · 52292 ms · 2026-05-10T19:25:59.712563+00:00 · methodology

discussion (0)

Reference graph

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