Operating room scheduling and adaptive control using a priority first fit decreasing heuristic
Bibliographic record
Abstract
Operating room (OR) scheduling is a critical factor affecting overall hospital performance. We examine OR scheduling from two perspectives. In the first perspective we propose a scheme for OR block scheduling that uses a heuristic developed for a three-machine flow shop where the three phases of the peri-operative process (pre-op, OR, and post-op) correspond to the three-machine flow shop. This approach facilitates a hospital-as-a-system perspective. The second perspective used to examine OR scheduling is adaptive control of the OR slate. Recognizing that there are many factors affecting OR throughput performance, especially preemptions from emergent and urgent cases, adaptive control of the OR slate is necessary. To realistically improve performance, adaptive control of the OR slate should incorporate constraints on how surgeries can be rescheduled. We examine the benefits from adaptive control of the OR slate that uses a priority first fit decreasing (PFFD) heuristic while incorporating constraints on OR slate rescheduling. The PFFD heuristic is a priority-driven variation of the classic FFD heuristic used in bin packing problems. We develop a scheme for OR block scheduling and our PFFD heuristic. We then demonstrate our PFFD heuristic in a simulation-based case study, and subsequently run a simulation using 1000 instances to test the performance of our PFFD heuristic in OR slate scheduling and OR slate adaptive control showing improvements in performance relative to the frequently used first-come-first-served rule.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".