Reliability analysis of supply chain for contingency operations
Bibliographic record
Abstract
In this paper, we propose a probabilistic analysis approach for assessing the reliability of supply chain for contingency operation consisting of several cities. We consider that the demand of cities and the quantity of products available at distribution centre are uncertain. Also, we analyze the case where the quality of products available at distribution center is considered uncertain. We evaluate the reliability of supply chain without making any particular assumption on normality of distribution of population demand and the quantity of products available at distribution center. Also, we analyse the problem with making correlation between demand of each city and quantity of products available at distribution center. To conduct a probabilistic analysis we consider the supply chain as a structure that undergoes an external load represented by the demand of population during the crisis period and resist to this load by its strength represented by the quantity of products available at distribution center. The reliability of supply chain for contingency operation is defined as the probability that the available inventory at distribution center meets all population demand during crisis period. The supply chain is considered “failed” if the quantity available at distribution center is less than population demand during crisis period. First Order Reliability Method is used to evaluate the reliability of supply chain.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".