Operating room planning with multiple downstream units
Bibliographic record
Abstract
Because of the importance of operating room management in hospitals, many researchers have attempted to develop mathematical programming models to use the available time in operating rooms as efficiently as possible. However, almost all researchers have considered only a single downstream unit in operating room planning. In this paper, we have developed a mixed-integer programming model for an operating room planning problem, which addresses multiple downstream units including wards, and ICUs. The proposed model allocates the patients to different operating rooms over a planning horizon while minimizing the sum of the opening cost of operating rooms, overtimes, and the cost of refusing patients, and the waiting cost of patients. The proposed model also addresses some other side features such as time windows for surgeries. We carried out some computational results and have performed an extensive sensitivity analysis on various cost parameters and also the capacity of each downstream. The computational results demonstrated that the proposed model is reliable and optimally solves instances with 315 patients in two minutes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".