A stochastic optimization model for designing disaster relief networks with congestion, disruption and distributional ambiguity
Bibliographic record
Abstract
We address a disaster relief network design problem that determines the location of emergency facilities and the allocation of victims to them in both the preparedness and response stages. The aim is to minimize the setup and access costs while reducing congestion in emergency facilities, modeled as an M/G/1 queuing network. A piecewise-linear approximation is developed to handle the nonlinear waiting cost term. Uncertainty about the occurrence and impacts of disasters, regarding both demand for relief services and disruption of the network nodes and arcs, is captured via a finite set of independent scenarios in a two-stage stochastic programming framework. Also, chance constraints are used to enforce coverage reliability targets for all demand nodes. Furthermore, since the probabilities of disaster scenarios are estimated based on a few historical events, a robust approach that uses a phi-divergence-based ambiguity set is applied to hedge against distributional ambiguity. A realistic case study of designing a relief network for earthquakes in Iran is employed to test and validate the proposed approach. The approximated model is found to be both near-tight and computationally efficient. Furthermore, sensitivity analysis for some key model parameters is conducted and insights for policymakers are drawn based on the results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".