Staffing call centers with uncertain demand forecasts:A chance-constrained optimization approach

摘要

The staffing problem is traditionally formulated as an optimization problem in which the objective is to minimize salary related costs subject to meeting pre-specified quality of service (QoS) targets for the various customer classes. The input to this optimization problem includes the salary costs, the QoS constraints, and various system parameters such as arrival rates, service times, and customers' patience. Solutions to this optimization problem lead to two actions: 1.The required number of agents with each given skill, and 2.A dynamic routing policy that defines how customers are assigned to agents in real time. In this paper, two important issues in call center workforce optimization are addressed, as the arrival rates are forecasted in advance hence not precise; and different customer classes have different QoS requirements so that they cannot be treated as a single customer class. The objective here, is to address this problem in a general setting taking in to account forecast uncertainty and general network structures. This approach has two specific features such as a chance-constrained formulation of the staffing problem with perfectly predictable rates.