A scenario-based stochastic programming approach for designing and planning wheat supply chain (A case study)
Bibliographic record
Abstract
Agri-food supply chains have received the attention of many researchers in recent years for various reasons, including food security and health-related issues. Wheat, as a staple food in many countries, is the most cultivated crop in the world. Due to the importance of wheat, this paper proposes a mixed-integer linear mathematical model for redesigning and planning of the wheat supply chain. The proposed model determines the location and capacity of new storage facilities while addressing supplier selection, ordering, storing, transportation, and distribution problems. This model considers the differences between long-term and short-term storage facilities and the quality of wheat. Moreover, the proposed model addresses the uncertainties associated with the quantity of domestic supply and demand through a stochastic scenario-based programming approach. Applicability of this model is investigated using real data from the wheat supply chain of Iran. Results show that seven new long-term storage facilities should be opened, which decreases total costs by 3.45 percent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".