Accounting for Time-Varying Queueing Effects in Workplace Scheduling
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
We developed a method for workforce scheduling that models both the structure of the set of permissible shifts, and the stochastic and time-varying demand process. A prototype implementation uses a genetic algorithm to search for good schedules, and evaluates the service level resulting from a schedule by numerically solving the equations of motion for a time-varying queueing system. Comparison with a traditional approach using a “stationary independent period-by-period” (SIPP) assumption to set staffing requirements and an integer program (IP) to choose shifts indicates that the traditional approach can significantly overestimate the service level that results from a schedule. Further, our method sometimes generates schedules that result in both lower labor cost and higher service level than those found with the SIPP-IP approach. An additional benefit of our method is its applicability in “rush hour” situations where the arrival rate to the system temporarily exceeds its capacity to serve customers.
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 | 0.019 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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