Strategic Idleness and Dynamic Scheduling in an Open-Shop Service Network: Case Study and Analysis
Why this work is in the frame
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Bibliographic record
Abstract
This paper, motivated by a collaboration with a healthcare service provider, focuses on stochastic open-shop service networks with two objectives: more traditional macrolevel measures (such as minimizing total system time or minimizing total number of tardy customers) and the atypical microlevel measure of reducing the incidents of excessively long waits at any workstation within the process. While work-conserving policies are optimal for macrolevel measures, scheduling policies with strategic idleness (SI) might be helpful for microlevel measures. Using the empirical data obtained from the service provider, we provide statistical evidence that SI is used by its schedulers to manage the macro- and microlevel measures. However, the company has no specific rules on implementing SI and the schedulers make decisions based on their own experience. Our primary goal is to develop a systematic framework for the joint usage of SI with dynamic scheduling policies (DSPs). We suggest to use threshold-based policies to intelligently combine SI and DSPs and show that the resulting policies provide an efficient way to simultaneously address both macro- and microlevel measures. We build two simulation models: one based on empirical data and one based on a randomly generated open-shop network. We use both models to demonstrate that an open-shop service network can be systematically and effectively managed to deliver improved service level by using SI.
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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 | 0.001 | 0.000 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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