Multi-operator driven iterated tabu search for inter-hospital operating room scheduling
Bibliographic record
Abstract
In this article, we investigate a new inter-hospital operating room (OR) scheduling problem (IHORS) taking into consideration the downstream recovery beds so that integrated decisions are made at the tactical and operational levels. To solve this challenging problem, we develop an effective multi-operator driven iterated tabu search (MOITS) algorithm comprised three search phases. The greedy construction phase employs a priority scoring rule to sequence patients, thereby generating a high-quality initial schedule. The multi-operator driven tabu search phase employs different move operators to manipulate surgeries among OR time blocks, change recovery hospitals of the scheduled surgeries, and change specialties of the OR time blocks. New evaluation functions are introduced to allow capacity violations during the search. The elite set guided adaptive perturbation phase uses historical information from a pool of best solutions to adjust the OR time blocks assigned to each specialty. Experimental results demonstrate that our proposed algorithm computes high-quality solutions, achieving objective gaps of less than 2% relative to the lower bounds. Moreover, it often requires substantially less time to reach comparable or better solution quality compared to the Gurobi solver. The effectiveness of the specific strategies incorporated into the algorithm is validated, and the managerial implications are explored.
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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 | 0.000 |
| 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 | 0.001 | 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".