A stochastic optimization approach for staff scheduling decisions at inpatient units
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper describes a solution approach for stochastic multi‐resource multi‐patient staff scheduling problems in inpatient units. Our solution approach has four steps. First, we classify patients into a number of groups with similar care‐provider requirements. Second, a predictive Markov model captures patients' flow in the inpatient unit and provides a prediction of the number of patients of each group in the future. This predictive model allows us to generate a potentially large set of possible system utilization scenarios over the planning horizon. Third, a mixed‐integer programming model with an expected value objective function seeks to minimize the expected over‐staffing and under‐staffing costs across all possible scenarios. Lastly, we use simulation to sample system utilization scenarios and the sample average approximation method to find a reliable and generalizable solution to the model across all possible scenarios. We evaluate the performance of the proposed solution using real data from the Children's Hospital of Eastern Ontario's inpatient mental health unit. The results show that the proposed approach significantly decreases the expected cost of the schedules in comparison to the traditional approaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it