A scenario-based possibilistic-stochastic programming approach to address resilient humanitarian logistics considering travel time and resilience levels of facilities
Bibliographic record
Abstract
There is a great deal of interest in addressing humanitarian logistics due to the need for emergency services in the case of disaster. Controlling both operational and disruption uncertainties in the emergency management is one of challenging topics lately to propose a robust plan for humanitarian logistics. Designing a robust and resilient humanitarian relief chain networks under both operational and disruptive risks can ensure the delivery of the essential supplies to beneficiaries. In this paper, a humanitarian logistic network design with multiple central warehouses and local distribution centres in an integrated manner is addressed by a novel scenario-based possibilistic-stochastic programming approach. The main real-life application of the proposed methodology is to consider the transportation network's routes after an earthquake to provide a plan against uncertainty in whole levels of supply chain along with its availability. To this end, a real case study of Mazandaran province in the north of Iran is provided to validate our methodology as well as a comprehensive discussion and managerial insights are concluded from 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".