Managing emergency logistics for hazardous materials with random severity level and link disruption: A distributionally robust optimization approach
Bibliographic record
Abstract
The potential tremendous threats caused by incidences associated with hazardous materials (hazmats) to the surrounding people and the environment necessitates a well-managed emergency logistics network, especially when the emergency is random. Focusing on the distributional ambiguity of the severity level and link disruption, we propose a distributionally robust optimization (DRO) model to construct a robust and reliable hazmat emergency response system. More specifically, a joint probability underlying the emergency demand and link disruption is defined to reflect the different characteristics of these two types of randomness. Then, based on the deterministic case, the DRO counterpart is formulated to minimize the total system cost and the worst-case risk over uncertainties. For the optimal solution, the DRO model is reformulated and solved using a Benders decomposition approach. We apply the proposed model and algorithm to a real-world case study. From a series of numerical experiments, managerial insights are derived to facilitate practical hazmat emergency management. • A DRO approach is applied to manage emergency response for hazardous materials. • The joint effect of emergency demand uncertainty and link disruption is considered. • A Benders decomposition algorithm is designed to obtain robust solutions. • A real-world case study is used to validate the model and to derive managerial insights.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".