Operating a biomedical samples laboratories network under stochastic demand
Bibliographic record
Abstract
Management of biomedical samples plays a central role in an efficient healthcare system and requires important resources. This paper addresses an assignment problem where several types of samples are collected at clinics and other collection centers, and then transported to medical laboratories to be analysed. For practical reasons, samples of the same type collected at the same collection center are consolidated and sent to the same laboratory. However, a collection center may send different types of samples to different laboratories. Moreover, demand for each type of sample at each collection center is uncertain but modelled by a known probability distribution. In this context, collection centers need to be allocated to laboratories in order to balance the workload between them while minimizing the total collecting distance. To tackle this problem, we first formulate it as linear integer model assuming deterministic demand. Then, a stochastic counterpart is presented, along with a solving method based on the sample average approximation technique (SAA). Numerical experiments inspired by a real-life case are conducted to illustrate how the proposed approach may be contribute to help decision makers to better manage the laboratories' capacity, increasing the system efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".