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 that 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 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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".