Home health care facility location problem under demand uncertainty
Bibliographic record
Abstract
The demand for home health care services is rapidly increasing due to the growing number of older people. The uncertainty surrounding this demand affects the network design processes and the performance of the home health care system in the long term. This study aims to address the issue of demand uncertainty in a home health care location problem. While decisions regarding the location of home health care facilities must be made immediately, the determination of distribution can be postponed until actual demand is observed. In such situations, minimax/maximin robust optimization methods are commonly employed to address uncertainty and facilitate informed decision-making, even in cases where there is limited information about future demand. However, these methods are often too conservative and may lead to suboptimal solutions. To tackle this issue, we propose a regret minimization method, which is reformulated as a robust model to overcome its intractability. Additionally, we propose a column-and-constraint generation algorithm to solve the robust optimization and regret minimization models. Finally, we conduct a comprehensive set of numerical experiments to compare the performance of the models in terms of solution quality and computational time. The results demonstrate that the regret minimization model enhances solution quality and consumes less computational time when reformulated.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".