A practical model for the supply chain growth optimization for automotive fuels in <scp>Mexico</scp>
Bibliographic record
Abstract
Abstract Profitability and energy efficiency depend on optimal operation of a supply chain network for automotive fuels in Mexico, even for clean energy expectations. This paper shows how decision‐making for fuels‐distribution network growth in Mexico points in the same direction, while the practical small optimization model, introduced in this paper, replaces a detailed model thereby offering great advantages to the analysts. The paper also describes the relevant strategies that were applied to ensure equivalences between the two models representing a supply chain. The model simplification not only reduces its size and computer time for execution but allows for the most relevant time reduction associated with preparing the data input to feed into the model, as well as in analyzing the results. Some distribution network growth options were evaluated by using both models with equivalent objective functions. Small model results give enough information for decision‐making support. The use of new facilities and economic benefits are similar to those obtained with the complex model. The main issues to take care of are bottleneck identification and commodities differentiation. If a binding constraint is removed in the small model, similar results are seen in the complex model when a specific bottleneck is improved. This is the first time that a practical model is used to evaluate multiple scenarios of the complex automotive fuel distribution network in Mexico.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".