Improvement in the operating room efficiency using Tabu search in simulation
Bibliographic record
Abstract
Abstract Purpose This paper aims to introduce the efficiency improvement in the operating room (OR) of a local hospital using the integration of simulation and optimization. Design/methodology/approach Based on the simulation model, a Tabu search (TS) algorithm is developed as an optimizer for the meta-heuristic optimization method to find the optimum configuration of resources for the OR operation. Findings The computational efficiency is improved for the optimum search. Results show that 21 percent more patients can be processed compared to the existing operation. The average time stay of patients in the OR is reduced by 17 percent. Research limitations/implications Limited resources considered in the model may limit the capacity of the proposed method, more resources including nurses, beds in post-operative units, and beds in inpatient wards will be included in the decision variables. Practical implications Long waiting lists in the OR lead to the low performance of healthcare systems. It is crucial to identify inefficiency and to improve the OR operation efficiently. Originality/value The TS-based heuristic optimizing method developed in this research shows the promise in time saving of the optimal solution search for the OR efficiency improvement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".