Coordinating a bi‐level blood supply chain with interactions between supply‐side and demand‐side operational decisions
Bibliographic record
Abstract
Abstract In most blood supply chains, blood centers and hospitals make individual decisions, resulting in an inefficient structure of the blood supply chain, which in turn renders supply and demand matching a challenging exercise. In this work, we make the very first attempt to optimize the interaction between blood centers and hospitals. To that end, this paper investigates collection, production, replenishment, issuing, inventory, and wastage decisions under three different blood supply chain channel structures, that is, the decentralized, centralized, and coordinated structures. We propose a bi‐level optimization program to model the decentralized system and use the Karush–Kuhn–Tucker optimality conditions to solve that. In such a system, hospitals tend to order more than their actual need, resulting in overcollection, overproduction, and high wastage rates. On the other hand, in a centralized system decisions are made by a central decision‐maker, which results in higher performance. Recognizing the challenges of implementing a centralized system, we design a novel coordination mechanism to motivate hospitals to operate in a centralized system. Analysis of a case study in Canada indicates that integration can significantly improve the performance of system; allowing substitution between blood products can decrease the total cost of the blood supply chain by 14.41%; an increase in supply or decrease in demand can be detrimental under inappropriate structure, facilitating coordination mechanism; offering subsidy beyond a threshold is not beneficial to the blood centers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".