Abstract
The problem is to optimize the appointments of customers with a single server to minimize a combination of idle time, waiting time, and overtime. We consider static appointments (come at 10:30am) and dynamic appointments (I will warn you one hour before you should come). The processing times of the customers are random and have unknown distributions. We design learning algorithms for situations where similar processing tasks repeat over time.