Dynamic Robust Multi-Class Advance Patient Scheduling
Bibliographic record
Abstract
In this work, we study an advance patient scheduling problem where patients of different classes have different service times and incur different waiting costs to the system. It is known in the literature that multi-class advance dynamic patient scheduling is a challenging problem due to the high variability in the daily arrival process of patients, as well as the high dimensionality of the problem. To overcome these challenges, we develop a novel dynamic optimization framework where the multi-class advance scheduling problem can be approximately decomposed to multiple single-stage stochastic programs. Furthermore, we develop a distributionally robust formulation and quantify uncertainty in arrivals by applying a risk-averse optimization approach. Exploiting patient-level offline data, we develop a data-driven algorithm to minimize the worst-case outcome that may happen due to the high variability in arrivals. We examine the performance of the proposed robust algorithm by leveraging the MRI data from hospitals in Ontario and show that the dynamic robust model outperforms the dynamic stochastic approach significantly. We also observe that the proposed robust model performs well compared to an offline policy, which is based on the full knowledge of the future arrivals.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".