Call-type dependence in multiskill call centers
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
The effect on multiskill call-center performance of pooling dependent call types is investigated. For this purpose, a copula-based modeling approach is used to provide multivariate models that take into account the call types’ asymmetric dependence structures found in empirical data. Then, the realistic input models of the call-type-dependent arrival processes are used in a simulation study to explore the sensitivity of the pooling decision to this dependence. We find that the widely used assumption of independence, as well as the misspecification of the dependence structure, can lead to substantial misestimation of call-center performance. This demonstrates the importance of modeling call-type dependence in stochastic simulation studies of call centers. We also show, through case studies, that pooling two asymmetric left-tail-dependent call types is more likely to lead to low agents occupancy; whereas the presence of right-tail dependence structure increases the risk of service-level shortfall. This work provides new managerial insights to improve decision making in determining which call types to merge in the same pool in multiskill call centers.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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