Using Strategic Idleness to Improve Customer Service Experience in Service Networks
Bibliographic record
Abstract
The most common measure of waiting time is the overall expected waiting time for service. However, in service networks the perception of waiting may also depend on how it is distributed among different stations. Therefore, reducing the probability of a long wait at any station may be important in improving customers' perception of service quality. In a single-station queue it is known that the policy that minimizes the waiting time and the probability of long waits is nonidling. However, this is not necessarily the case for queueing networks with several stations. We present a family of threshold-based policies (TBPs) that strategically idle some stations. We demonstrate the advantage of strategically idling by applying TBP in a network with two single-server queues in tandem. We provide closed form results for the special case where the first station has infinite capacity and develop efficient algorithms when this is not the case. We compare TBPs with the nonidling and Kanban policies, and we discuss when a TBP is advantageous. Using simulation, we demonstrate that the analytical insights for the two-station case hold for a three-station serial queue as well.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".