Comparison of Stochastic Programming Approaches for Staffing and Scheduling Call Centers with Uncertain Demand Forecasts

Abstract

We consider the staffing and shift-scheduling problems in call centers and propose a solution in one step. It consists in determining the minimum-cost number of agents to be assigned to each shift of the scheduling horizon so as to reach the required customer quality of service. We assume that the mean call arrival rate in each period of the horizon is a random variable following a continuous distribution. We model the resulting optimization problem as a stochastic program involving joint probabilistic constraints. We propose a solution approach based on linear approximations to provide approximate solutions of the problem. We finally compare them with other approaches and give numerical results carried out on a real-life instance. These results show that the proposed approach compares well with previously published approaches both in terms of risk management and cost minimization.