Integrated planning decisions in the broiler chicken supply chain
Bibliographic record
Abstract
Abstract In the poultry industry, the meat market requires a careful coordination of the broiler chicken supply chain comprising breeders, hatcheries, farms, slaughterhouses, wholesale, and retail vendors. Aside from the inherent challenges of coordinating a supply chain, animal husbandry systems face additional complex tasks. The lack of integrated decisions within the poultry chain could lead to a production plan that (a) does not comply with the biosecurity standards required in meat production for human consumption at the farms; (b) violates the production and inventory capacities at the slaughterhouses; and (c) does not meet the demand of customers. To streamline the supply chain, we propose a mixed‐integer linear programming model that supports production planning and scheduling decisions in broiler chicken production facilities. In addition, we embedded the mixed‐integer programming model in a rolling‐horizon scheme to improve scalability and to avoid the myopic effect of time‐indexed optimization models that put too much emphasis on a specific time period. We present the results of a case study in a poultry company in Santa Marta (Colombia), where we reach profit improvements that range from 7% to 57% with a reduction in inventory costs that range from 30% to 60%, while simultaneously meeting stringent technical, tactical, and biosecurity constraints.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".