Proactive and reactive inventory policies in humanitarian operations
Bibliographic record
Abstract
Inventory planning for the pre-and post-disaster phases of disaster relief lifecycle is a challenging problem associated with the humanitarian relief supply chains. In this work, two mathematical models are presented encompassing the whole disaster relief lifecycle. By accounting for holding costs of perishable supplies, a two-stage stochastic programming model is first developed by which the inventory prepositioning locations, inventory levels, and shortterm distribution quantities are determined. For the recovery phase, this research adapts the well-known continuous review (Q, r) inventory model for relief warehouses while accounting for the inherent epistemic uncertainty in the required data by using the fuzzy programming. A case study of Iranian Red Cross is also provided to illustrate the applicability of the first model and to demonstrate how it supports the two first phases of disaster lifecycle. Additionally, a numerical example is presented to demonstrate the applicability of the (Q, r) model for the recovery phase. Lastly, the impact of penalty costs on the solutions is discussed.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".