Enhancing humanitarian logistics under uncertainty: A data-driven distributionally robust optimization approach with worst-case mean-CVaR
Bibliographic record
Abstract
With the rise in global disasters, improving humanitarian supply chains and evacuation planning is essential for saving lives and delivering help quickly and fairly. This study proposes a model that integrates facility location, relief item distribution, and evacuation operations while accounting for critical social parameters such as demographic vulnerability and regional accessibility in affected areas. The inter-shelter collaboration logistics strategy is incorporated into the framework to address challenges in optimizing resource allocation and minimizing disruptions caused by blocked roads and uncertain demands. This research also develops a data-driven two-stage distributionally robust optimization (DRO) model, employing the worst-case mean-conditional value-at-risk criterion to ensure robustness against extreme scenarios. The model’s performance is assessed through out-of-sample analysis, demonstrating the DRO model’s enhanced robustness and effectiveness compared to the traditional two-stage stochastic programming model. The model is applied to the real case of the Fort McMurray wildfire in Alberta, Canada, to validate its practical applicability in disaster management. The results emphasize that prioritizing relief items, addressing social factors, and employing the inter-shelter collaboration strategy together improve evacuation efficiency and enhance resilience in disaster management, with the inter-shelter collaboration strategy contributing, for example, to approximately a 40% reduction in the unmet demand for a critical item.
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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 | 0.002 | 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 | 0.001 | 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".