Name
Abstract | ||
|---|---|---|
The benefits of cross-training in terms of increasing responsiveness to demand fluctuations have been studied extensively in the literature. In this work, we study another important advantage of cross-training due to customer misclassification, i.e. a caller declares to face a certain problem (e.g. a hardware problem) where in fact another problem persists (e.g. a software problem). In call centers that apply no cross-training, misclassified calls need to be rerouted to agents who are able to serve the true problem, whereas cross-training enables agents to serve different problem types which reduces cycle times. We introduce two-type queueing models to study the effects of customer misclassification on cross-training in call centers. We observe that, if only a third of the agents is cross-trained, high increases in model performance can be confirmed, whereas little benefit is added by higher amounts of cross-training. We also study the effects of routing policies on cycle times. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-07001-8_58 | Operations Research Proceedings |
Field | DocType | ISSN |
Mathematical optimization,Software Problem,Queueing theory,Mathematics,Cross-training | Conference | 0721-5924 |
Citations | PageRank | References |
1 | 0.37 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Schwab | 1 | 1 | 0.37 |
| Burak Büke | 2 | 31 | 3.21 |