Large-scale decomposition strategies for collaborative operating room planning and scheduling
Bibliographic record
Abstract
Operating rooms (ORs) play a substantial role in hospital profitability, and their optimal utilization is conducive to containing the cost of surgical service delivery, shortening surgical patient wait times, and increasing patient admissions. We extend traditional single-hospital operating room scheduling to a coalition of multiple collaborating hospitals in a strategic network. Using data from the University Health Network (UHN), in Toronto, Ontario, Canada, we propose new centralized approaches to elective and operating room scheduling when multiple collaborating hospitals are involved. We formulate the OR scheduling problem based on location-allocation problems in supply chain management. We ensure caseload balancing among collaborating hospitals in macro and micro levels. We additionally incorporate patient-to-surgeon allocation flexibilities, surgeon-to-hospital allocation flexibilities, and surgeon schedule tightness. Furthermore, we tackle single-hospital multiple specialty OR scheduling problems and single-hospital single-specialty multi-resource constrained OR scheduling problems. We develop novel logic-based Benders decomposition and branch-and-check techniques for these problems and we show\nthat our approaches are up to two orders of magnitude faster than directly solving the mathematical models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".