Blood platelet bank inventory management: an approximate dynamic programming approach
Bibliographic record
Abstract
Blood platelets are precious and highly perishable; their supply and demand suffer from significant variation. Consequently, the inventory management of platelets is an actual, contemporary prob- lem of considerable human interest. Although many researchers have solved a plethora of inventory models, their solutions have faced various challenges. This dissertation models some of these chal- lenges, alongside expenses and stock levels. This dissertation is based on four key objectives: (1) to develop a blood platelet inventory model that can represent an actual blood bank inventory, while overcoming the problem's curse of dimensionality; (2) to look for the best issuing policy based on the proposed model that can serve different incoming blood platelet demands; (3) to analyze the effect of having a new, artificial blood platelet alongside the existing natural eight blood types; and (4) to enhance the proposed model for a dual-supplied regional blood platelet bank that serves a network of hospitals. Blood platelet inventory management model is a multi-period, multi-product model that considers the eight natural blood types with uncertain demand, and deterministic lead times, alongside the artificial platelet and patients right to refuse it. The study is supported by both a review of literature and a testing data provided by the Canadian Blood Service. The findings show that modeling blood platelet inventory management, including the eight blood types and their ages, represents the actual-life model without any need for downsizing. It also leads to significantly reductions in shortages and outdates while increasing reward gained and maintaining minimal inventory levels. Compared to a single supply model, the dual supply model give less shortage and outdate rates. The regional blood bank inventory model considers the fact that patients have the right to refuse transfusion using artificial blood platelets. Finally, if the percentage of artificial supply in the inventory is more than 30% and the rate of patient acceptance is more than 30%, then both outdate and shortage percentages are below 1%.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".