A Market-Based Scheduling Mechanism Design for Cost Reduction in Home Health Care
Bibliographic record
Abstract
We consider a decentralized home health care scheduling setting where a health care agency assigns a group of independent health care practitioners to home visits. Health care agency’s objective is to minimize the overall payments for covering all planned visits, while a practitioner’s cost is cons idered as his/her private information unknown to the agency. The key challenge here is how to allocate home visits to practitioners such that high quality solutions, which benefit both the health care agency and the practitioners can be obtained. To tackle this challenge, we design a market-based mechanism in the format of an iterative auction which enables the computation of cost effective schedules through multilateral negotiation among the health care agency and practitioners. The effectiveness of the designed mechanism is evaluated through a computational study conducted in a proof of concept prototype environment. Our experiment results show that the designed scheduling mechanism achieves on average 96% efficiency compared with the optimal solutions. In addition to experiment results, we prove that the mechanism can always compute optimal solutions to a special case of the home health care scheduling problem.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".