Spare parts provisioning for multiple<i>k</i>-out-of-<i>n</i>:G systems
Bibliographic record
Abstract
This article considers a repair shop that fixes failed components from different k-out-of-n:G systems. It is assumed that each system consists of the same type of component; to increase availability, a certain number of critical components are stocked as spare parts. A shared inventory that serves all systems and/or reserved inventories for each system are allowed; this is called a hybrid model. Additionally, two alternative dispatching rules for the repaired component are considered. The destination for a repaired component can be chosen either on a first-come first-served basis or by following a static priority rule. The analysis gives the steady-state system size distribution of the two alternative models at the repair shop. Numerical examples are performed that minimize the spare parts held while subjecting the availability of each system to exceed a targeted value. It is shown that a hybrid priority policy is better than a hybrid first-come first-served policy, unless the availabilities of systems are close.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".