Quarantine-aware home healthcare routing and scheduling: a bi-objective approach
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has presented an unparalleled challenge to the healthcare sector, emphasizing the vital role of Home Healthcare (HHC) services in delivering essential medical care to patients and the elderly within their homes. This approach has proven to be the most effective means of adhering to quarantine protocols. In response, healthcare managers/decision-makers face the imperative of cost reduction, service quality enhancement, and the assessment of patient and nurse satisfaction. To address these pressing needs, our research introduces a Mixed Integer Linear Programming (MILP) model tailored to the COVID-19 era. The model's central objective is to augment the operational efficiency and patient satisfaction of HHC organizations while ensuring strict adherence to quarantine regulations. It builds upon the foundational Vehicle Routing Problem with Pickup/Delivery and Time Window formulation, encompassing critical aspects like patient and caregiver classification, work regulations, workload balancing, and multi-depot capabilities. The bi-objective model considers the primary constraints associated with quarantine conditions. For model resolution, we employ the augmented ɛ-constraint (AUGMECON) method and conduct several sensitivity analyses related to workload balancing's impact on other decision variables. To illustrate the problem’s complexity and assess the effectiveness of the proposed MILP model across various scenarios, 15 additional sample instances have been solved and documented in the Appendix. In conclusion, our research not only provides essential managerial insights but also highlights avenues for future research within this crucial domain.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".