An integrated inventory and distribution planning problem for the blood products: An application for the Turkish Red Crescent
Bibliographic record
Abstract
This study considers an integrated inventory planning and distribution problem based on an applied case at the Turkish Red Crescent’s Central Anatolian Regional Blood Center. We define two echelons, the first echelon being the regional blood center and the second echelon being the districts. The blood products are perishable so that the outdated products are disposed of at the end of their lives. We aim to minimize the cost of inventory keeping at both echelons, the shortage, and disposal amounts at the second echelon. We consider two distribution strategies: all deliveries are realized by the regional blood center (current implementation), and the deliveries are directly from the regional blood center or the other districts. We develop a mixed-integer linear programming model for each strategy. Our experimental results show that the decentralized strategy brings significant cost reductions over the centralized strategy. The mathematical model for the centralized distribution strategy can handle large-sized instances. On the other hand, the model for the decentralized distribution strategy is more complex and could not handle large-sized instances in our pre-specified termination limit of two hours. For large-sized instances of the decentralized distribution strategy, we design a decomposition-based heuristic algorithm that benefits from the optimal solutions of the original model and finds near-optimal solutions very quickly.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".