Multiple operation theatre scheduling for mitigating the disturbance caused by emergency patients
Bibliographic record
Abstract
Scheduling emergency patients is a problem that most hospitals struggle to solve without disturbing elective surgery patients' schedules. The present work undertakes this problem and considers scheduling elective patients surgeries in multiple operation theatres to mitigate the possible disturbance caused by emergency patient arrivals. The resultant problem has been termed as multiple operation theatre problems with total expected disturbance (MOTED). The number of elective surgeries and their corresponding surgery times are known and given in advance. However, emergency patient arrivals are stochastic in nature, which is tackled through scenario-generation techniques. The model assumes that emergency case scenarios can be predicted from historical data, and determines the sequence of elective patients in a multiple operation theatre such that the sum of the total expected disturbance (TED) caused by emergency patients and the total completion time of elective surgeries is minimized. The disturbance minimization increases the satisfaction level of patients, physicians and other medical staff, and indirectly reduces the overtime costs. The work provides an optimal algorithm for the MOTED problem with a single single-operation theatre. Three heuristics and two metaheuristics have been proposed to solve the complete MOTED problem. The metaheuristic involves particle swarm optimization (PSO) and ant colony optimization (ACO). An extensive numerical experiment is performed using 48 randomly generated problem instances.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".