Optimizing multi-vehicle emergency perishable material distribution across multiple centers with a freshness consideration
Bibliographic record
Abstract
Optimizing the distribution of perishable emergency materials in the aftermath of sudden disasters holds significant theoretical and practical value. This paper begins by defining the freshness of perishable emergency materials and develops a continuous piecewise function to describe the changes in quality and quantity of these materials at different time stages. Additionally, it considers distinct delivery requirements for each demand point regarding the same perishable emergency materials. Under the minimum freshness constraint, a vehicle distribution optimization model is established for perishable emergency materials, considering multiple distribution centers and multiple vehicles. Depending on whether the distribution center has an adequate number of vehicles, the solution can be classified into three scenarios. In the case of insufficient total emergency supplies but sufficient vehicles, an accurate algorithm is designed to address the model. Alternatively, when both the total emergency supplies and vehicles are insufficient, an approximate algorithm is developed to solve the model. Finally, when the total quantity of perishable emergency materials is inadequate, and some vehicles are sufficient while others are not, the problem is transformed into one of the first two situations to find a solution. To validate the accuracy of the model and the effectiveness of the algorithm, an analysis is conducted using the flood-stricken area in Shouguang, Shandong as a case study.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".