Designing a bi-objective and multi-product supply chain network for the supply of blood
Bibliographic record
Abstract
During the past few years, operations research applications in health care operation management have grown quickly.On the other hand blood as a perishable, valuable and lifesaving product is one important asset of any healthcare center.Therefore, designing a blood supply network comes to importance.It also should be noted that a blood supply chain comprises specific modifications.This study intends to locate blood bank components in a network, and to determine the allocations among the network components.The supply chain components considered in this study are donation sites, testing and processing labs, blood banks, and demand points.It is known that demand centers such as hospitals and clinics highly depend on blood products and any deficiency in procurement can even result in a person's death.Thus, in the last layer of the considered network a transshipment sub-network is considered between demand points.Most of the intricacies in problem formulation of blood supply chain are regarded in this study; cases such as blood wastage, blood product decomposition in lab facilities, and transshipments between demand points.Due to the fact that for such an important and lifesaving supply chain the aim would go beyond minimizing cost, another objective function is presented for the problem.Hence, to obtain a Pareto solution for both objective functions ∊-constraint method is utilized.Finally, to demonstrate the applicability of the problem, the model is implemented on a number of problem sets.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".