Parametric Stochastic Programming Models for Call-Center Workforce Scheduling

We develop and test an integrated forecasting and stochastic programming approach to workforce management in call centers. We first demonstrate that parametric forecasts can be used to drive stochastic programs whose results are stable with relatively small numbers of scenarios. We then extend our approach to include forecast updates and two-stage stochastic programs with recourse and provide a general modeling framework for which recent, related models are special cases. In our formulations, the inclusion of multiple arrival-rate scenarios allows the call centers to meet long-run average QoS targets, while the use of recourse actions help them to lower long-run average costs. Experiments with two large sets of call-center data highlight the complementary nature of these elements.