A Fix‐And‐Optimize Matheuristic to Solve the Location‐Allocation of Vaccination Facilities: Case of Jalisco, Mexico
Bibliographic record
Abstract
ABSTRACT Approximately 755 million cases and 7 million deaths have been reported since the COVID‐19 pandemic. Based on the availability of vaccines to contain the spread of COVID‐19, various vaccination plans have been implemented globally, some more effective than others, depending on each country's social, territorial, economic, and political circumstances. In Mexico and other Latin American countries with similar situations, COVID‐19 vaccinations have almost exclusively relied on ephemeral mass vaccination facilities and existing healthcare infrastructure. However, other countries (e.g., the USA and Canada) have opted into pharmacy‐based immunization (PBI), which uses community and/or chain pharmacies as vaccination facilities to provide more accessible immunization services. This research aims to evaluate the feasibility and expected performance if PBI had been used in Mexico. Therefore, we propose a mathematical formulation to address the location–allocation problem underlying the pharmacy selection and the assignment of individuals to them. However, since commercial solvers cannot efficiently address the resulting formulation for real‐sized instances, the formulation is embedded into a heuristic fix‐and‐optimize scheme to explore the solution space more efficiently. The case of Jalisco, Mexico, is used to illustrate the performance of the proposed approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".