An Outer Approximation Method for Scheduling Elective Surgeries with Sequence Dependent Setup Times to Multiple Operating Rooms
Bibliographic record
Abstract
In this paper, operating room planning and scheduling problems have been studied. In operating room planning, the allocation of patients to operating rooms and their sequencing are critical in determining the performance of operating rooms. In this paper, three surgery scheduling decisions are considered, including the number of operating rooms to open, the allocation of surgeries to operating rooms, and the sequencing of surgeries in allocated operating rooms. All the surgeries under consideration are elective, and surgery durations are considered deterministic. Further, it is considered that the surgeries have different specialties, and each operating room can accommodate a particular specialty of surgeries, i.e., heterogeneous operating rooms are considered in the current study. Before performing a surgery, setup time is required for operating room turnover and sterilization, and it is considered sequence dependent. A mixed integer nonlinear programming (MINLP) model is developed to minimize the overtime costs of operating rooms for allocation and surgery sequencing with sequence dependent setup times. An outer approximation (OA) method is proposed to solve the problem near optimally. Experiments are conducted to compare the performance of the proposed OA method with the standard mixed integer nonlinear programming model. Computational results show the efficiency of the proposed OA method. Later, a case data from a case hospital is collected and a case study is solved.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".