Production policy optimization in the systems with perishable products under seasonal demand
Bibliographic record
Abstract
Manufacturing systems are often subject to dynamic market conditions, characterized by demand variations over time. Production policy optimization in this situation is more challenging than in case of stationary demand rate. When manufactured products are perishable, the demand variations are of particular importance, as they often result in additional losses due to disposal of perished products. In particular, that is the case in food and pharmaceutical industry. Both these aspect must be taken into account for production policy optimization. It is particularly important when the production facility is failure-prone. The rationale here is that conventional approach is based on setting the safety (hedging) inventory level in order to cope with potential equipment failures leading to shortage. For perishable products, however, this approach needs revision due to eventual deterioration of products kept in stock longer than the shelf-life limit. To address the production control problem in this context, a 3-steps procedure is developed. First, the hedging inventory level that varies in time adapting to demand variations is computed. Second, the upper limit for the products kept in stock (perishable inventory limit), which depends on the shelf-life and demand variation pattern (thus also varies in time) is determined. Third, that perishable inventory limit is shown to determine an upper bound for the hedging level. The proposed production policy adapts to demand variations and accounts product shelf-live; it is optimal and results in no perished products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".