Bibliographic record
Abstract
Abstract This paper considers an infinite‐horizon repairable‐item inventory problem wherein the number of repairable units returned to the system in a given period is assumed to be equal to random proportions of the serviceable units issued in the current and the last M periods. We formulate the problem as an infinite‐horizon Markov decision model and propose an algorithm for finding a steady‐state optimal inventory policy, which minimizes the expected long‐run total discounted cost. For speeding up the convergence of the algorithm we present an improved algorithm that utilizes error bounds. We illustrate the algorithms with numerical examples. Résumé Cet essais examine un problème d'inventaire d'articles réparables à horizon infini oú le nombre d'unité réparables renvoyés dans le système à l'intérieur d'une période donnée devrait être égale à des mesures publiées ayant été prises au hasard parmi les unités utilisables lors de la dernière périodes M et lors de celle en cours. Nous formulons le problème comme un modèle de décision à horizon infini de type Markov et proposons un algo‐rythme pour une politique d'inventaire optimale station‐naire, ce qui minimise le total attendu à long terme du coût escompté. Pour accélérer la convergence de l'algo‐rythme, nous présentons un algorythme amélioré qui utilise les limites d'erreurs. Nous illustrons les algo‐rythmes avec des exemples numériques.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".