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Machine learning-based agent staffing under uncertainty: The case of a relay call center
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NETL
NSTL
Elsevier
Classical queueing models fail to properly staff non-conventional call centers with complex internal structures. This is either due to the difficulty of finding suitable models whose underlying assumptions hold, or due to certain elements of the call center not being modeled such as caller patience times. Relay call centers, service providers that connect two different interested parties with one another through telecommunication channels, present a prime example of non-conventional call centers. Working on the study case of a relay call center for the deaf community, Erlang C, one of the most commonly used call center staffing formulae, fails to generate agent staffing that meets our target performance criteria for quality of service. We propose a machine learning-based approach leveraging an available log of historical data. Upon comparing the proposed approach's capability of performance evaluation and agent staffing to that of the Erlang C model and a baseline data-driven model, results indicate our approach's staffing superiority. Considering uncertainty within the system variable predictions carried out prior to the staffing phase, our approach generates agent staffing which enables us to meet our global quality of service objective.
Call centerMachine learningData-driven decision makingAgent staffing under uncertainty
NETL
NSTL
Elsevier
