Medical Supplies Delivery Route Optimization under Public Health Emergencies Incorporating Metro-based Logistics System
Bibliographic record
Abstract
To effectively mitigate the spread of infections during public health crises, precise and timely distribution of medical supplies is crucial. This paper proposes the integration of the metro-based underground logistics system (M-ULS) into the delivery process to address the vehicle routing problem (VRP). Considering various factors related to the metro-based VRP in public health emergencies, we formulate a mixed-integer nonlinear function model that aims to minimize total delivery costs while maximizing the load factor of medical vans and the average demand index concerning demand urgency and satisfaction rate of medical centers. To achieve this, we develop an improved adaptive genetic algorithm (IAGA) that incorporates adaptive crossover, adaptive mutation, and an elitist strategy. A case study is conducted to verify and analyze the performance of the optimized model and solving algorithm. Extensive numerical experiments are carried out to assess the economical and efficient advantages of M-ULS and the computational efficiency and solution quality of IAGA. Sensitivity analysis is conducted to evaluate the impact of medical van capacity on the routes. Lastly, we consider inter-medical-center transfer (IMCT) to further enhance emergency health response.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".