Application of Operations Research methods in operating room scheduling - a short survey
Bibliographic record
Abstract
The surgical services provided in the operating rooms of the hospital are an essential part of the healthcare system. These services are usually life-threatening and time-sensitive, and require highly trained surgeons and staff, as well as the latest medical equipment. Furthermore, because of the high cost of these resources, hospitals can have only a limited number of operating rooms and staff members. Thus, it is crucial to optimize various aspects of operating room functions to maximize overall utilization.This survey summarizes various optimization models proposed in the literature for such problems faced in operating rooms. The goal of this work is to provide researchers with a guide for further research in the field. Methods: This survey includes articles from Pubmed, since 2010. The search queries were related to the terms scheduling operating room, optimization model, and queuing. More than 400 articles were found, and the authors filtered the articles based on their relevance to this survey.The analysis found that a) the studies are usually very specific to optimizing a particular problem related to the operating room, b) datasets are not available in the literature and it is difficult to conduct comparative analysis, similarly c) the source code is not available on publicly available repositories like GitHub, and d) it is difficult to replicate the studies and establish benchmarks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".