Combining simulation and integer programming IP techniques to achieve realistic optimality

Abstract

In recent years, call centers have been considered as an integral part of the modern businesses. High performance of call centers, therefore, is crucial to ensure high level of customer satisfaction in today's competitive market. In order to achieve that high performance, managers of call centers face a very difficult set of challenges. They need to achieve low operating costs and high service quality. The proposed framework combines statistical, simulation, and integer programming (IP) techniques in achieving realistic optimality. The framework begins by developing stochastic statistical data models for call center operations parameters which are divided into service demand (arrival volumes) and service quality (service times, abandonment volumes, and patience time) parameters. These data models are then used to run a simulation model that is used to determine the minimum staffing levels in daily, hour periods. Finally, these staffing levels are considered as input to an IP model that optimally allocates the service agents to the different operating shifts of the working day.