Pooling strategies for call center agent cross-training

The efficiency benefits achievable via cross-training in call and service center environments where agents serve distinct customer types are investigated. This is achieved by first considering specialized agents grouped into N departments according to the customer type they serve. Then, cross-training policies that pool a set of departments into a single larger department that serves all of the pooled call types according to either a first-come-first-served or non-pre-emptive priority service discipline are examined. The impact of system parameters, such as the number of servers, mean service times and service time coefficient of variation, on the decision of which departments to pool in order to minimize the expected delay in the system are characterized by comparing the proposed queueing models via standard queueing approximations and numerical analysis. The results show that if the mean service times of the departments that will be pooled are similar, pooling the departments with the highest service time coefficient of variation reduces the expected delay the most. Sufficient conditions for the mean service times to be considered similar are also provided.