A stochastic mixed-integer model to support foodbank resources prepositioning during the prelude to a natural disaster
Bibliographic record
Abstract
A key strategic issue in pre-disaster planning for humanitarian logistics is the pre-establishment of adequate capacity and resources that enable efficient relief operations. Foodbanks must review their decisions and replan their activities upon the arrival of catastrophic events, such as earthquakes or floods. With the aim to support managers in the adaptation of their network and preparedness decisions during the prelude to the event, this paper presents a scenario-based stochastic mixed-integer optimisation formulation that aims to minimise the maximum amount of unfulfilled relief needs considering uncertainty both on the demand as well as on the availability of the infrastructure. The formulation was applied to the case of hurricane Odile that struck the Baja California Peninsula, Mexico, in 2014. Numerical experiments demonstrate that the solution reached by the proposed mathematical formulation improved the actual decisions that were made during the event. Further comparisons and analyses are presented. [Submitted: 19 October 2021; Accepted: 24 February 2022]
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".