Coordinating a bi‐level blood supply chain with interactions between supply‐side and demand‐side operational decisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it