Real-Time Estimations for the Waiting-Time Distribution in Time-Varying Queues

Customers’ waiting times are the most commonly used performance data to measure the quality of service in service systems such as call centers and healthcare. Unlike stationary queueing models where customers’ waiting times are statistically similar, the prediction of waiting times is far less straightforward in time-varying queues having nonstationary demand (i.e., arrival rate) and supply (i.e., number of servers). In this paper, we develop a novel methodology for more accurately computing the wait time distribution in a time-varying queueing system. We design extensive simulation experiments to evaluate our prediction methods. In addition, we discover that the waiting-time prediction is highly sensitive to the work-releasing policy of the staffing plan, i.e., the rule under which the number of servers changes in time.