Staffing a call center with interactive voice response units and impatient calls
Bibliographic record
Abstract
A call center consists of telephone trunk lines, a switching machine known as the automatic call distributor (ACD), an interactive voice recording unit (IVRU), and telephone sales agents. Calls enter the center whenever a trunk line is available; otherwise it is lost. Once a trunk line is seized, the call is instructed to choose among several options provided by the call center, via the IVRU. After completing the instructions at the IVRU, the call may leave the center or be routed to an available agent. If all agents are busy, the call is queued at the ACD until one is free. While waiting for the agents, calls may abandon the queue if their waiting time becomes unreasonably long. The reason for impatience and abandonment varies from call to call and it is difficult to quantify. In this paper, we assume that each call abandons the queue independently of each other while waiting for agents after a random amount of time.With this assumption, a serial network model is introduced to determine the optimal quantities of the number of trunk lines and agents subject to given service level. It is shown that abandonment of calls will influence the waiting time and hence the number of agents needed to meet a specific service level. With abandonment, this model provides a reasonable way to determine the number of trunk lines and agents required simultaneously. Numerical examples will illustrate the effects of abandonment on design parameters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".