Stochastic Goal Programming and a Metaheuristic for Scheduling of Operating Rooms
Bibliographic record
Abstract
Health care systems in Canada provide benefits to patients but have issues with costs and wait lists. Long wait lists negatively affect patients’ welfares. This in turn can increase costs because conditions can develop into more complicated ones over time. Operating rooms in a hospital are responsible for a significant portion of both costs and benefits; therefore, finding ways to use them more efficiently can reduce both the waste of tax dollars and the lengths of wait lists and can improve patients’ welfares. In this research, a stochastic weighted goal programming model is proposed to perform elective surgery scheduling under uncertainty of both surgical durations and patient lengths of stay. The model generates a Master Surgical Schedule that schedules surgical teams in operating room blocks in a way that minimizes four objectives, which are the deviations between the targeted number of surgeries and the actual number of surgeries performed, the deviations between the targeted number of hours for surgeries and the actual number of hours used for surgeries, the maximum expected number of patients in the recovery ward over the course of the planning horizon, and the difference between the maximum and minimum expected numbers of patients in the recovery ward over the course of the planning horizon. In addition, the impact of cancellations on the schedule is taken into account. A simulated annealing metaheuristic is developed to find near-optimal solutions. Discrete event simulation is used for validation and to demonstrate the system of operating rooms and recovery ward beds to relevant stakeholders in the health care sector.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".