Dynamically dealing with requests in a stochastic multi-period home healthcare problem with consistency constraints
Bibliographic record
Abstract
This paper analyzes a Multi-Period Stochastic Vehicle Routing Problem in the healthcare sector. Patients with unknown locations and demands ask for domiciliary care services with unknown temporal distributions. Requests from patients arrive over time to a nurse agency that has to plan the activities of a fleet of nurses over several days. Based on daily information, the agency decides if a new patient can be accepted, earning the corresponding revenue, or assigned to an external provider. To guarantee high service satisfaction, the agency schedules the nurses' routing by guaranteeing consistency in nurse-patient assignments. The problem aims to plan nurses' routing to satisfy all requests of accepted patients while maximising the total profit measured as the difference between collected revenue and travelling costs. We propose different solution methodologies that either sequentially make short-sighted decisions or use a scenario-based strategy, leveraging historical data to predict future requests. All algorithms make use of an Adaptive Large Neighborhood Search and are validated on medium-sized instances. Managerial insights on the impact of consistency on the profit and its relation to date flexibility in patients' requests are provided. Interesting rules of thumb are derived from a case study conducted in the city of Brescia, Italy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".