Impact of Vertically Integrated Road Transport on Brazilian Sugar Export Logistics: A Mathematical Programming Application
Bibliographic record
Abstract
Brazil currently exports 73% of the sugar produced at harvest. Approximately 75% of those exports are transported to the port of Santos and 18% to the port of Paranaguá for oversea shipment. Transportation to the two ports is mainly through the use of outsourced road transport vehicles. This study analyzes the impact of vertically integrating road transportation operations on the cost to transport raw sugar to the ports. Specifically, the study consists of an evaluation of the economic costs and benefits arising from sugar shippers using their own fleet of vehicles to transport their product to Santos and Paranaguá. Many papers have reduced logistics costs using strategies that involve a change in transport mode, most often to the railways. Although a change in modality may reduce logistics costs, vertically integrating the transport fleet into the producing company may also effectively lower costs. This article aims to (i) assess economic impacts on sugar export logistics in Brazil’s South-Central region if agro-industry shippers (mills) vertically integrated their road transport and (ii) identify the optimal regional allocation of vertically integrated logistics operations. The analysis was conducted using a linear programming model designed to identify minimum, multimodal sugar export logistics costs taking into account private and outsourced shipping fleets. The model was programmed and processed with the GAMS modeling system using a CPLEX solver. The results indicate: (i) the competitive economic transportation radius using a mill’s private trucking fleet is 420 km or less, (ii) the best strategy to minimize road transportation export logistics costs in Brazil’s South-Central region was obtained by using a private fleet 46.30% of the time, which, if all road shipping services had been outsourced, would reduce road transport costs 5.01%, and (iii) there are a number of sugar-producing meso-regions in which the use of vertically integrated transportation operations reduced logistics costs by over 10%, even if all road transportation services were vertically integrated. The results are expected to be used to promote sugar transportation through the optimized use of private shipping fleets and stimulate further discussion of the advantages and disadvantages of vertically integrated product transport operations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".