Admission Control in the Presence of Arrival Forecasts with Blocking-Based Policy Optimization

This paper presents a simulation based policy optimization scheme for performing queuing admission control in the presence of noisy arrival forecasts. The forecast models considered for arrival times go beyond the no noise and no show forecast models treated in the literature and incorporate realistic features such as decreasing accuracy profile for jobs arriving farther in future. Assuming access to forecasts for arrivals within a look-ahead window, the paper proposes optimization over a policy class which approximates combinations of threshold and blocking type policies in the literature. While threshold policies tend to be optimal for admission control problems without forecasts, blocking-based policies have been effective in settings where exact arrival data is known. Exact knowledge on future arrivals is however unrealistic and a key novelty is the use of robust optimization to compute blocking policy statistics. Numerical experiments demonstrate good reductions in waiting costs achievable by incorporating forecast data.