The Robustness and Sustainability of Port Logistics Systems for Emergency Supplies from Overseas
Bibliographic record
Abstract
When the epidemic comes, in addition to eliminating people’s panic, quickly enacting corresponding laws and implementing corresponding policies, and isolating infected people, providing emergency supplies are of course essential. The purpose of this article is to study the robustness and sustainability of the port logistics system for outbreak emergency supplies from overseas. This paper analyzes the sustainable development capability of the port logistics system for outbreak emergency supplies from overseas and formulates response strategies and robust collaborative optimization methods. The optimized and robust system is obtained through formula derivation and analysis, which realizes the coordinated optimization of emergency logistics infrastructure positioning and emergency rescue vehicle path positioning and minimizes the economic loss caused by the outbreak. Research data show that the shortest path can be planned between each material supply location. The research results show that the proportion of demand fluctuations during the outbreak is 6.5%, the time window needs to be controlled between 0 and 600, and the robustness and sustainability of the port logistics system have saved the time of the entire process by about 45%, greatly optimizing the delivery route and delivery time. The robustness of the logistics system can be widely used in emergency events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".