A Dedicated Lane Analysis for Supply Chain Resilience in the U.S.-Mexico Border: Cost-Comparison and Simulation Models
Bibliographic record
Abstract
Supply chains have been actively developing and implementing strategies to enhance resilience in response to various disruptive events like the COVID-19 pandemic, hurricanes, geopolitical tensions, and climate change. These strategies aim to address demand and supply imbalances, logistical challenges, and policy restrictions encountered in transborder commerce. One area significantly impacted by such disruptions is cross-border trade between the US and Mexico. Understanding these strategies is crucial for achieving supply chain resilience, defined as the ability of a supply chain to quickly adapt to sudden disruptions without affecting the flow of goods. In both the US-Mexico and US-Canada borders, Free and Secure Trade (FAST) lanes have been established for commercial vehicles. Additionally, in 2022, the government of Nuevo León, Mexico, introduced a new approach by inaugurating a dedicated lane exclusively for northbound commercial traffic related to Tesla. Such dedicated lanes in queueing systems represent innovative methods to enhance performance measures by prioritizing the needs of specific companies. This thesis aims to investigate the impact of a unique strategy implemented at the Colombia-Solidarity Port of Entry in the US-Mexico border: the introduction of a dedicated lane exclusively for the suppliers of a single company. The study seeks to develop methods for analyzing this strategy, including a cost-comparison model and a simulation model. The cost-comparison model will assess the cost-effectiveness of the dedicated lane compared to regular lanes, while the simulation model will explore various scenarios involving an increase in the flow of the dedicated lane to evaluate its positive or negative effects on the supply chain. The methodology will analyze different traffic flow scenarios for the dedicated lane and normal lanes at the Colombia-Solidarity Port of Entry. It will also compute other metrics to compare the system under different congestion scenarios. The thesis will determine whether the strategy implemented enhances resilience in the supply chain or if alternative strategies need to be considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".