Inbound call center problems need decisions in four main areas; the number of incoming calls, necessary number of agents to handle these calls, shift starting times for the operations and the number of agents to be allocated to each shift. Call center models mainly cover these areas seperately. This study approaches to the problem with an integrated Markovian approximation, heuristic supported simulation and optimization model. Incoming demand data is adapted from a private Turkish Bank and an application of the model is conducted. Moreover, probabilistic lunch breaks are introduced to the optimization part of the model. Finally, the model can be used as an easy decision support tool for the call center managers.
Inbound call center problems need decisions in four main areas; the number of incoming calls, necessary number of agents to handle these calls, shift starting times for the operations and the number of agents to be allocated to each shift. Call center models mainly cover these areas seperately. This study approaches to the problem with an integrated Markovian approximation, heuristic supported simulation and optimization model. Incoming demand data is adapted from a private Turkish Bank and an application of the model is conducted. Moreover, probabilistic lunch breaks are introduced to the optimization part of the model. Finally, the model can be used as an easy decision support tool for the call center managers.
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