We describe roles for statistical ranking and selection procedures within and after the termination of algorithms for optimization via simulation. We also display some ranking and selection procedures that have been specifically designed for this purpose.

We develop stochastic models of time-dependent arrivals, with focus on the application to call centers. Our models reproduce essential features of call center arrivals observed in recent empirical studies, namely, a time-varying arrival intensity over the course of a day, and nonzero correlation between the arrival counts in different time periods within the same day. For each of the new models, we characterize the joint distribution of the vector of arrival counts with particular focus on characterizing how the new models are more flexible than standard or previously proposed models. We report empirical results from a study on arrival data from a real-life call center, including the essential features of the arrival process, the goodness-of-fit of the estimated models, and the sensitivity of various simulated performance measures of the call center to the choice of arrival process model.

We consider a telephone call center operating in blend mode, i.e., with both inbound and outbound calls. Our objective is to allocate a number of agents such that some service requirement is satisfied. We have taken two approaches in analyzing the staffing problem: we develop a simulation model of the call center, which allows us to do a what-if analysis, and we also propose continuous-time Markov chain (CTMC) queueing models, which provide good approximations of certain performance measures of the system.

Pricing has become one of the main challenges of the networking community and is receiving a lot of interest in the literature. In this presentation, we analyze the so-called Paris Metro Pricing scheme which separates the network into different and independent subnetworks, each behaving equivalently, except that they charge their customers at different rates. In our model, each subnetwork is represented by a single bottleneck queue, and the ``customers'' (data packets) choose their subnetwork taking into account not only the prices, but also the expected delay, which is supposed to have an economic impact. We obtain some necessary and sufficient conditions for the stability of the system; we analyze the problem of maximizing the network revenue and compare it with the case of a single network, and present several extensions of the model. Numerical results illustrating some key aspects of the system will be provided.