Customer Scheduling in Large Service Systems Under Model Uncertainty
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
In the realm of many-server service systems, scheduling often necessitates the use of simplifying assumptions regarding service times to facilitate model development. However, empirical observations indicate that these assumptions may not accurately mirror real-world situations. In their paper titled “Customer Scheduling in Large Service Systems Under Model Uncertainty,” Chai, Sun, and Abouee-Mehrizi introduce an innovative approach to assist decision makers in devising high-quality scheduling policies for large service systems. This approach involves optimizing against an imaginary adversary through a robust control framework that is based on a manageable and simplified model. The imaginary adversary’s role is to exploit the potential vulnerabilities of a scheduling rule by dynamically perturbing the simplified model within an uncertainty set. This uncertainty set can be estimated using data-driven methods. Extensive numerical experiments, including a case study utilizing a data set from a U.S. call center, provide substantial evidence supporting the effectiveness of our framework.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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