The Value of Partial Resource Pooling: Should a Service Network Be Integrated or Product-Focused?
Bibliographic record
Abstract
We investigate how dynamic resource substitution in service systems impacts capacity requirements and responsiveness. Inspired by the contrasting network strategies of FedEx and United Parcel Service (UPS), we study when two service classes (e.g., express or regular) should be served by dedicated resources (e.g., air or ground) or by an integrated network (e.g., air also serves regular). Using call center terminology, the question is whether to operate two independent queues or one N-network. We present analytic expressions for the delay distributions and the value of network integration through partial resource pooling. These show how the value of network integration depends on service quality (speed and reliability of service) and demand characteristics (volume averages and covariance matrix). Our results suggest that network integration is of little value and operating dedicated networks is a fine strategy if the firm primarily serves express requests with high reliability and if the correlation with regular requests is not strongly negative. In contrast, network integration offers significant gains for firms serving primarily regular requests, almost independent of correlation. Our analysis provides the intuition behind these findings in terms of three main drivers of integration value: arrival pooling, the substitution effect, and the correlation effect.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".