Outpatient appointment scheduling with urgent clients in a dynamic, multi‐period environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Time waiting for service is a major concern for consumers, and excessive waiting for a pre‐scheduled appointment is especially annoying. This is an on‐going problem because appointment scheduling is a challenging task, mainly due to the uncertainties associated with service times. Prior studies have focused mainly on a single scheduling period (i.e. either a morning or afternoon); this paper uses a more realistic model that represents an on‐going, multi‐period scheduling environment where clients can be scheduled days or even weeks into the future. Two main objectives will be considered; the best scheduling rule to use in a multi‐period environment, and the best placement of appointment slots that are left open for urgent clients. Both of these have been studied in a single period environment, and results here will be compared to those. It will be shown that in some cases earlier findings from the one‐period environment are robust and perform well in a multi‐period environment, while in other cases the one‐period findings do not apply.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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